5g problem solving techniques

Researchers work to solve 5G network problems when it matters: right now

Kelly Izlar

14 Dec 2022

Share on Facebook
Share on Twitter
Copy address link to clipboard

5G can enable smart cities, virtual realities, and self-driving cars — but will these applications be convenient and safe to use if network connection is delayed? Commonwealth Cyber Initiative researchers from Virginia Tech developed a methodology that provides optimal solutions to network problems on the fly and in real time.

The fifth generation of mobile network (5G) is bringing more applications, devices, and users into network operations. But spiking demand can stress local networks, creating bottlenecks that are narrowed by safety-critical or other high-priority tasks that need to happen as soon as possible.

Computer engineers such as Virginia Tech’s Tom Hou have dedicated their careers to fine-tuning network parameters and components to get ever closer to peak performance — an endeavor further complicated by real-time demands.

“The holy grail of my research has always been timing,” said Hou, the Bradley Distinguished Professor of Electrical and Computer Engineering in the College of Engineering.

Timing constraints have forced computer engineers to modify their algorithms to stay within suboptimal network thresholds, which limits functionality and throttles performance.

This changed in 2018, when Hou’s research group hit upon a methodology that pulled real-time into range.

“This was a major breakthrough,” said Hou. “With support from the Commonwealth Cyber Initiative in Southwest Virginia , we elevated network optimization to a whole different level: solving problems in the field in real time.”

Ultra-high precision for ultra-low latency

In the context of 5G, timing is tied up with the concept of latency. Latency refers to time duration, or how long it takes to complete a certain task or step in a process. Minimizing latency is an attempt to reduce delay. When it comes to 5G, a delay of even a few milliseconds can make a difference to an industrial automation system or a power grid , for instance.

As a delay stretches, not only will user experience degrade, but risk to device, information, or safety increases.

“Think about industrial automation or autonomous driving , which require information to be transported over different systems very quickly to ensure tight synchronization,” said Hou. “Reaction time on the road or in a warehouse is critical to preventing accidents, making latency of utmost importance.”

To deliver that kind of end-to-end latency on the order of millisecond, scheduling from the 5G base station has to be on the same order or even lower.

How it started

In 2018, Hou and his team were designing a system to meet the stringent timing requirements of new radio access technology for the 5G mobile network. To support applications with ultra-low latency, the minimum timing resolution for optimal 5G New Radio performance was capped at 125 microseconds — almost 10 times faster than what was possible with 4G LTE.

Up until then, no one had been able to deliver optimal scheduling in that interval.

The Virginia Tech team proposed a scheduling algorithm that incorporated a graphics processing unit (GPU) — a specialized circuit that uses parallel computing to accelerate workloads in high performance computing.

Parallel computing isn’t a new technology. A supercomputer processes computations in parallel with thousands of central processing units, but it’s expensive, cumbersome, and can’t be accomplished locally — by the time a base station outsources a task to the cloud and receives the results, it’s far too late to meet real time scheduling needs.

Originally designed for graphics rendering, a GPU isn’t in the same league as a supercomputer in terms of processing capability. It wasn’t designed for scientific computation or solving complex optimization problems, but when coupled with Hou’s new scheme, it doesn’t have to be.

Hou and his team developed a multistep methodology that breaks down a big problem into a smaller set of sub problems and then zeros in on the sub problems that are likely to yield the most promising results. For this manageable set of small problems, custom solutions can be developed by a GPU processing in parallel.

“With this technique, even a low-end GPU can find near-optimal solutions within the sub-millisecond time window,” said Hou.

Hou’s team’s innovation rocked the field of wireless network optimization.

“Probably the most important feature of 5G is the ability to communicate with low latency, and Professor Hou’s work makes this feasible,” said Jeff Reed, the Commonwealth Cyber Initiative's chief technology officer and the Willis G. Worcester Professor of electrical and computer engineering at Virginia Tech.

GPU manufacturer Nvidia showcased Hou's work, which was carried out in collaboration with fellow Commonwealth Cyber Initiative researcher Wenjing Lou in computer science. The invention was awarded a U.S. patent as it was applied to 5G schedulers. But this was just the beginning.

“We thought — wait a minute, there’s more than just scheduling for 5G problem,” said Hou. “We identified the key steps, theorized the technique, and implemented it to solve other wireless networking and communications problems with similar mathematical structure.”

Scaling up to secure autonomous vehicles

Armed with a process that brings real-time solutions into reach, Hou’s research group was ready to apply it to complex problems in different domains. With continued support from the Commonwealth Cyber Initiative in Southwest Virginia, the team is tackling radar interference in autonomous vehicles.

In addition to camera and lidar, autonomous vehicles monitor road conditions with radar because it’s not finicky about weather or lighting conditions. Day, night, rain, or snow — radar is robust.

It is, however, susceptible to interference. A radar bounces a signal off nearby objects and then measures the reflected signal to determine what’s on the road and around the vehicle. When there are too many radars on the road, signals bounce around willy-nilly, compromising a radar’s normal capability.

“Such radar-to-radar interference offers a relatively easy way to unleash a cyberattack on an autonomous vehicle and compromise safety,” said Hou.

Hou is applying the methodology to sort through which signals should matter to an autonomous vehicle, mitigating high levels of interference in real time, in-vehicle, and with an affordable GPU.

The application of the scheduling algorithm as applied to radar mitigation will be published through IEEE Radar Conference proceedings in early spring, and a patent has been filed with Virginia Tech Intellectual Properties. The Commonwealth Cyber Initiative provides funds for the translation of research into practice through programs such as “ Innovation: Ideation to Commercialization ” and patent support costs.

Edge computing and elsewhere

Hou and his team also are applying their technique to task-offloading for edge computing, which involves determining which tasks should be processed on a local device and which should be offloaded to the 5G base station.

“Our new real-time optimization methodology has many, many applications. It’s opened up a new life, certainly for me, but also for other researchers doing network optimizations,” said Hou. “We keep seeing new places where this methodology can be applied, and in many cases, finding groundbreaking solutions along the way.”

Lindsey Haugh

Autonomous Vehicles
College of Engineering
Commonwealth Cyber Initiative
Commonwealth Cyber Initiative, Southwest Virginia Node
Computer Science
Cybersecurity
Electrical and Computer Engineering
LINK+LICENSE+LAUNCH
Research Areas
Research Frontiers
Security Frontier

5G is Finally Here, But Questions and Issues Remain

5G has been publicized as a substantial change in mobile networking—promising faster download speeds, real-time data-sharing, and network capacity. As such, the technology is expected to transform mobile networking and create new economic opportunities . Despite delays with the rollout in both the U.S. and parts of Europe, 5G has reached much of the globe. South Korea, the U.S., and China are leading the implementation with around 80-90% population coverage. Although Europe overall is trailing behind, Switzerland has reached 90%. A 2021 report from Ericsson projects 5G will account for nearly half of all mobile subscriptions by 2027 while also becoming mainstream in each of the report’s ten studied regions. Though challenges persist for deployment globally, technology providers are optimistic about the future of 5G.

Forecasts for Industry

While the majority of 5G-supported or enabled applications today are consumer-based (think mobile streaming, augmented reality, virtual reality, and gaming), many experts believe the real money will be in enterprise applications of 5G. In its 5G value report, KPMG estimated the business-to-business case at US$4.3 trillion . Across industries—from factory automation and large-scale video surveillance to remote surgery and connected smart cities— there are a number of potentially groundbreaking use cases.

5G enables fast, secure, and pervasive connectivity across smart networks and Internet of Things (IoT) devices. When combined with artificial intelligence, 5G can enable unparalleled productivity and efficiency. Apart from the global estimate, five industries are poised to see incremental growth : Industrial Manufacturing, Connected Healthcare, Intelligent Transportation, Environmental Monitoring, and Gaming. KPMG estimates the market across the ecosystem for these five industries will be worth more than US$500B by 2023.

Managing Expectations

Before organizations adopt 5G, they should understand the differences between 4G and 5G network architectures to understand how both could affect business operations. Small cell technology enables 5G to provide more cell density and enhance network capacity. While 4G technology also made similar promises, experts anticipate 5G will succeed where its predecessor falls short. However, it is essential to understand that there are still 5G issues, and it may take years to reach its full potential.

Health Concerns Regarding 5G

When you use your phone to communicate with other devices, cellular data is sent through radio frequencies (RFs). “The radiofrequency 5G is higher than the previous iterations of wireless communication, including 4G and 3G”, says Henk De Feyter, Ph.D., an assistant professor of radiology and biomedical imaging at Yale School of Medicine in New Haven, Connecticut. In a world of propaganda and misinformation about 5G, how are policymakers and the public supposed to make sense of any individual claim? (For example, various internet theories have tied 5G technology to cancer and COVID-19.)

Learn more about this topic by watching 5G Demystified: Health and Safety of 5G , an on-demand LinkedIn Live recording where our speakers discuss the science and standards of human health effects from electromagnetic radio waves in 5G communications. Watch now>>

More Ways to Explore Current 5G Issues with IEEE

Depending on what you read, 5G is either a threat to society, the impetus for the next industrial revolution, or a marketing ploy to get us to buy new phones and tablets. Seldom has an emerging technology been so widely known yet so misunderstood. IEEE Future Networks and IEEE Educational Activities have developed a free virtual event series, 5G Demystified , where experts make sense of the technology’s potential.

Check out the events in this series:

Hype vs. Reality of 5G
Broadband is Infrastructure?
Health and Safety of 5G

Plus, check out these online course programs and earn continuing education credits while growing your knowledge of telecommunications technology!

Bridging the 4G/5G Gap: Telecommunications Roadmap for Implementation: This two-part course program provides a historical overview of 4G/5G, explains the legislative and regulatory background, showcases the scientific evidence surrounding wireless facilities’ impact on property value and human health, and offers a roadmap to deploy wireless facilities. Learn more>>

5G Networks: Produced in partnership with Nokia, this online course program provides an in-depth view of performance requirements, future scenarios, and the roadmap to 5G implementation. It also explores the intricacies of 5G standardization by the 3rd Generation Partnership Project (3GPP) and the IEEE 802 LAN/MAN Standards Committee. Learn More>>

Ericsson. (November 2021). Ericsson Mobility Report .

Goss, Michaela. (November 2022). 5G vs. 4G: Learn the key differences between them . TechTarget.

Holt, Alex. (June 2020). The 5G edge computing value opportunity . KPMG.

Laurence, Emily. (31 May 2022). Is 5G Making You Sick? Here’s What Experts Say . Forbes.

Vella, Heidi. (17 February 2022). 5G vs 4G: what is the difference? Raconteur.

5G , 5g health risks , 5g networks , IoT , mobile network , telecommunications

No comments yet.

Leave a reply click here to cancel reply..

Name (required)

Email (will not be published) (required)

Subscribe to get the best articles, infographics, and course news delivered straight to your inbox.

This website will offer limited functionality in this browser. We only support the recent versions of major browsers like Chrome, Firefox, Safari, and Edge.

What to expect from the AI/ML in 5G Challenge

The 2nd Artificial Intelligence/Machine Learning (AI/ML) in 5G Challenge is set to conclude in December, capping a successful debut last year.

Organized by the International Telecommunication Union (ITU) as part of its AI for Good initiative, the competition sees participants around the world solving real-world problems by applying machine learning in communications networks. Reinhard Scholl, Deputy Director at ITU’s Telecommunication Standardization Bureau, shares the journey of the Challenge and what’s coming up next.

This year’s AI/ML in 5G Challenge is the second such competition. What were your learnings from the first year?

Reinhard Scholl: When we started the Challenge, we had no idea where it would be going. It was an adventure and turned out to be such a positive experience. Last year, we had participants from 62 countries.

This year, we have participants from 82 countries, with the Grand Challenge Finale scheduled on 14 December.

We never expected such big numbers. We were also surprised by the large number of problem statements – between 15 and 20 each year – that we were able to offer so far. And we are grateful to this year’s sponsors, Xilinx and the Republic of Korea’s Ministry of Science and ICT.

We also published a special issue on AI and machine learning solutions in 5G and future networks in the ITU Journal Future and Evolving Technologies , with a selection of peer-reviewed papers submitted by Challenge participants.

We are on the lookout for new problem statements for the third Challenge .

One thing we are hoping to offer next year is computing resources for participants who might not have the support of a rich university or company. Training machine-learning models can take a lot of time, and several participants informed us that they don’t have the resources to run meaningful models. So, we are working on that.

How does the Challenge align with the work of the ITU Telecommunication Standardization Sector (ITU-T) and the AI for Good platform?

ITU-T does a lot of technical work related to machine learning in its focus groups – six of which have AI or machine learning in their title – and in its study groups. The specifications of the focus groups are generally turned into ITU standards (“ITU-T Recommendations”).

The most popular standard is the “Architectural framework for machine learning in future networks including IMT-2020” ( ITU-T Y.3172 ), which gives a common nomenclature and primer on how to talk about machine learning in communication networks, so that it can be used by anyone for any network.

Some of the solutions submitted to problem statements in the ITU AI/ML in 5G Challenge reference ITU standards on machine learning. Some have generated contributions to the respective focus groups or study groups – and attracted new ITU-T members. We have run over 50 one-hour, in-depth talks so far – by researchers on machine learning and communication networks – on the AI for Good Discovery Channel , a fabulous resource on what the future of communication networks will look like. We have similar “Discovery Channels” on Trustworthy AI , AI and Health , as well as AI and Climate Science . In January 2022 we are going to launch a Geospatial AI Discovery Channel.

What are the opportunities and challenges you see for AI and ML in the real-world 5G sector?

Network operators have used machine learning for some time, but not at the network level. They have used it to analyse the churn rate or to segment their customers. But applying it at the networking level is complicated. Applying machine learning in communication networks is much more difficult than in computer vision or natural language processing, because time scales in a communication network span many orders of magnitude, ranging from parameters which change on an annual basis, like your subscription to a telecom provider, to milliseconds, like resource block allocations in radio access networks) – for which you then have to retrain your machine learning model on a millisecond basis.

As networks get more and more complicated, machine learning will be essential to make sense of the plethora of data being collected. On the other hand, machine learning could also be useful in the standardization process.

For now, standards are produced by people, who meet, make proposals, negotiate, and agree on a certain outcome. But the resulting protocols are often ambiguous and suboptimal, leading to increasing costs in testing and implementation. Part of this process could be taken on by machine learning, where the algorithm proposes a solution. There have been some attempts to do this, but there’s still quite a long way to go.

What else can we expect?

We are branching into a new Geospatial AI Challenge that draws on location-based data. We have launched a call for problem statements. ITU and the World Health Organization (WHO) meanwhile are working, through their joint focus group, on an incredibly ambitious AI for Health Assessment project. When we take a prescribed medicine or vaccine, there is a sense of trust in the process and in the institutions.

But why would you trust an AI model looking at your X-rays? What does it take to trust a company that has an AI solution to detect skin cancer? The focus group is building a benchmarking framework that allows people to trust in AI health solutions.

The ITU-WHO focus group will come up with a process, guidelines, and best practices to ensure trust in AI solutions.

In addition, it’s developing a platform where a company can submit and test solutions using undisclosed data. A score is generated and published on a leader board, which also allows a regulator to know how good the solution is. You must design a process that allows experts to come to an agreement and then build that into the platform. The prototype will be ready in a matter of weeks. Then it needs to be transformed into a professional platform, which will cost serious money. We are going to start an AI for Good Fund to secure donations for projects like the AI for Health Assessment Platform, along with other work, such as the AI and Road Safety global initiative established in October.

Learn more about the Artificial Intelligence/Machine Learning (AI/ML) in 5G Challenge.

Privacy Overview

Global Atlanta

The world is here.

Solving for 5G: How Math Modeling Can Improve Modern Communications

From COVID to Cell Phones

Modeling entails a particularly useful set of skills today, in light of the current pandemic, for it permits policy makers to foresee the consequences of decisions, as well as educate all of us on the nature of the pandemic and what we can do about it.

But not all mathematicians are epidemiological modelers, and there is a myriad of other big questions toward which we can aim our academic ammunition.

In the modern world, there may be no more vital infrastructure than that of telecommunications, and Swedish companies like Ericsson are world leaders, particularly in the hardware that relays 5G mobile signals, promising exponential increases in mobile Internet speed.

But as many know, questions linger about deployment: particularly how radiofrequency signals will behave in the built environment.

Last June, I was part of a collaborative research project along with Elena Cherkaev ( University of Utah ) and Niklas Wellander (FOI Sweden ) charged with implementing mathematical modeling to ultimately help improve modern communication systems.

Funded in part by an American-Scandinavian Foundation grant , I went to Linköping, Sweden , southwest of the capital in Stockholm , with the particular goal of studying Passive Intermodulation (PIM) from a rigorous mathematical perspective.

Signal Integrity

PIM occurs when multiple signals are active in a passive device (such as a cable) that exhibits a nonlinear response. Frequently, PIM occurs as a result of multiple cell phone providers sharing certain paths in wireless networks. To the everyday user on their cell phone, this can manifest as decreased data speed or even dropped calls.

The focus of our research is the relationship between PIM, temperature effects and rough surfaces. In particular, taking into account the heat generated by radio-frequency signals, we are working to understand how surfaces with sharp corners or angles affect signal integrity.

While the research is ongoing, the impact is clear: understanding the effects of geometry (such as buildings in a large urban environment) on signal integrity is important for the development of improved cellular networks. What we find will affect everyone: From the drivers of future autonomous vehicles to the companies relying on 5G technology to track their products, as well as patients enjoying newly enabled telemedicine sessions.

Indeed, the state of Georgia has already been involved in 5G innovation: the Marine Corps Logistics Base in Albany was selected by the Department of Defense as a testing ground for 5G-enabled warehouses.

To unlock the advantages of these and other technologies, we will need to continue to drive new and collaborative thinking around their usage — perhaps even breaking out a mathematical model every now and then for problems that don’t seem like they can be solved on paper.

About the author:

Dr. Eric Stachura has a B.S. and Ph.D. in Mathematics from the University of Illinois at Chicago and Temple University, respectively. He is currently an Assistant Professor of Mathematics at Kennesaw State University in Atlanta. His research interests include problems at the interface of mathematics and physics, in particular nonlinear electromagnetics and geometric optics. During his studies, he has spent two significant periods abroad studying and conducting research: one at the Eidgenossiche Technische Hochschule (ETH) in Zurich, Switzerland , and one at the Instituto de Ciencias Matematicas (ICMAT) in Madrid, Spain.

In addition to his research, he is interested in innovative teaching techniques. As part of the Silver’19 cohort of the Mathematical Association of America’s Project NExT, he has incorporated numerous active learning strategies into his classes.

GSU-CIBER is the presenting sponsor of Global Atlanta's Education Channel . Subscribe here for monthly Education newsletters.

Atlanta International School is the presenting sponsor of Global Atlanta's Technology Channel. Subscribe here for monthly Tech newsletters.

You must be logged in to post a comment.

SIGN UP FOR GLOBAL ATLANTA NEWSLETTERS

Join more than 10,000 readers receiving timely updates on the latest international business news in Atlanta.

5G Coverage Problem Analysis and Optimization Scheme Exploration

Ieee account.

Change Username/Password
Update Address

Purchase Details

Payment Options
Order History
View Purchased Documents

Profile Information

Communications Preferences
Profession and Education
Technical Interests
US & Canada: +1 800 678 4333
Worldwide: +1 732 981 0060
Contact & Support
About IEEE Xplore
Accessibility
Terms of Use
Nondiscrimination Policy
Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. © Copyright 2024 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

Search Filters Clear All

Next on 5g: enabling supercharged problem-solving.

Daniel Mason Contributor

5G is much more than just a new carriage network. The 5th generation mobile network is an enabler that will supercharge a wider range of emerging technologies, such as artificial intelligence, just-in-time supply chain management and augmented reality.

In fact, far more than just giving us quicker internet service, 5G is likely to transform every aspect of our lives, in ways we’ll be able to see and also other more covert ways.

It will also likely play a central role in Industry 4.0, the large-scale digitisation of manufacturing and industrial processes, a sector which has previously lagged behind in terms of the digitisation of services.

5G can be a game-changer for the mining sector and manufacturers, enabling better automation and monitoring.

Across the board, from government services to monitoring container ships, 5G will turbo-charge new technologies and make many more a reality, according to Verizon regional vice-president for the Asia Pacific Rob Le Busque.

“5G enables and supercharges many of them,” Mr Le Busque said. “That is a meta-change that we’ll see across industry sectors.”

Mr Le Busque discussed the opportunities presented by 5G with Nokia’s head of global enterprise & public sector for the Asia Pacific Stuart Hendry, and InnovationAus’ publisher Corrie McLeod in a recent Age of Trust podcast.

For traditional industries like mining, 5G will help improve safety by reducing the need for humans to visit hazardous industrial environments, while the technology will also enable the use of drones to inspect shipping containers and detect damage before they disrupt operations, for example.

“If a damaged container is picked up, it can clog up the whole system. Drones can be used to inspect containers as they leave the ship, and if they are damaged, put them to one side,” Mr Hendry said.

“Not only is that better for safety, it saves the operators millions of dollars, and it also ends arguments between the various insurers as to whether the container was damaged onshore or on the ship.”

To explore this range of opportunities, Verizon has established the Operation Convergent Response, which will bring together other organisations including Nokia to explore and test potential emergency response applications in real-world situations, with first responders.

This will bring together more than 100 organisations from the first responder community, and more than 120 partners, Mr Le Busque said.

“We road-test new platforms with 5G as an enabler,” he said. “Being able to do this in a live simulated environment gives us so much learning and accelerates change in a lot of areas that will help to make the world a safer place.”

Thanks to its ultra-high bandwidth and ultra-low latency, the possibilities of 5G are virtually limitless, and will only become clear in future years.

“When we contemplate the problem-solving capacity of 5G technology, I don’t think the true impact has really even been imagined yet,” Mr Le Busque said.

For businesses, the challenge is to now identify the opportunity of 5G best suited to their company, and then capitalise on these future applications. 5G can support three business priorities: improving efficiency, creating better customer experiences and finding ways to create more frictionless commerce.

“If we think about those three very broad parameters, then you can describe many, many applications,” Mr Le Busque said.

Most importantly, it’s time to think of 5G as an emerging technology enabler with boundless potential rather than just a new mobile network.

The Age of Trust podcast series was produced as a partnership between Verizon and InnovationAus.com. The statements and opinions expressed in this podcast do not reflect the views or opinions of Verizon or its affiliates.

Do you know more? Contact James Riley via Email .

SunDrive fabricates first ever full-size solar panel

Australia secures expedited data-sharing deal with the US

How 5G technologies can be implemented more efficiently

Knowing where to place 'small cells' will make all the difference

As the name suggests, 5G is the fifth generation of cellular mobile communications. The key benefits of 5G over 4G LTE are much higher data rates (1-20 Gbit/s), much lower latency (1 ms), and increased capacity as the network expands.

What does this actually mean for the average consumer?

Higher data rates allow consumers to download content more quickly e.g. a consumer could download a full HD movie in less than 10 seconds on a 5G network vs. ~ 10 minutes on a 4G network.
Lower latency means users will experience less delay / lag when requesting data from the network - a latency of milliseconds, which are imperceptible to a human.

More broadly, the 5G network will advance mobile from largely a set of technologies connecting people-to-people and people-to-information to a unified connectivity fabric connecting everything to everything (X2X); 5G will act as a critical enabler for Massive IoT, Connected Autonomous Vehicles (CAV), remote critical control etc. Cars will be connected to the roads and cities they are navigating; doctors to the medical devices of their patients; physical infrastructure and assets to those tasked with maintaining and managing them. The promise of 5G is to enable billions of new connections that are fast, secure, and instantaneous.

Understanding the vast amount of data generated from the 5G revolution intelligently will be a key challenge in unlocking the full potential of 5G to vastly improve our lives, as promised.

Simulated realities

At SenSat, a leading European AI company based in Shoreditch, London, we are developing powerful technologies that can ingest and understand numerous real-time datasets, root them contextually in physical real world geometry, and then generate tremendously valuable spatial intelligence. How?

SenSat create ‘digital twins’ of real-world locations by capturing high resolution 2D and 3D data, using leading data-capture technologies. For example, as the UK's largest drone data provider and the UK Department for Transport’s Pathfinder Infrastructure Partner, we can autonomously capture physical geometry 400x faster than other manual data capture techniques.

We then infuse real-time spatial datasets onto these digital twins, creating ‘simulated realities’, which are exact digital environments that mirror what is happening in the real world.

Are you a pro? Subscribe to our newsletter

Sign up to the TechRadar Pro newsletter to get all the top news, opinion, features and guidance your business needs to succeed!

SenSat can then use sophisticated machine learning techniques using these simulated realities to solve complex spatial optimisation problems, allowing for faster and better decision-making.

Barriers to 5G rollout

Indeed, the rollout of 5G in the UK is one of these complex spatial optimisation problems.

The first commercial rollout of 5G networks in the UK will utilise relatively long wavelength bands of spectrum that have already been auctioned / are expected to be auctioned in 2020.

As Qualcomm’s tests in Frankfurt and San Francisco demonstrated, achieving required data rates for 5G in the real world (Gbit/s) will require using mmWave spectrum (>30GHz).

However, mmWave spectrum suffers from short transmission paths and high propagation losses. The shorter wavelength (measured in mm) makes mmWave highly sensitive to physical structures, facade materials, temporary obstructions, weather, etc. - all of which cause absorption and refraction, resulting in significant signal attenuation / loss.

As a result, line of sight (LoS) transmission from base stations (called ‘small cells’) to devices will be required to maintain sufficient 5G data rates. This will require mass densification of urban areas with new small cells to propagate mmWave 5G.

The ultimate ‘cost’ of rolling out 5G will be proportional to the number of small cells needed to be installed for this ‘mass densification’ of urban areas - reducing the cost of this rollout requires reducing the number of small cells needed to be installed, optimising their location, and improving the logistical efficiency of installation.

Optimising the location of small cells

This complex non-linear optimisation problem requires an understanding of:

mmWave propagation: because of its extreme sensitivity to the physical environment, ensuring adequate propagation to provide sufficient 5G data rates is a non-linear problem e.g. doubling the data rate may require a >2x increase in the number of small cells, depending on the complexity of urban areas. Data needed: permanent physical topography (vegetation, buildings), temporary topography (mass urban transit patterns), weather etc.
Data demand: understanding data demand/use in various locations is also key to understanding the required number of small cells. Data needed: land use, land type, footfall data etc.
Logistical constraints: improving logistical efficiency of the rollout of small cells will reduce overall rollout cost e.g. reducing the number of building owners, and local authorities that need to be contacted for permission to install small cells will lower the cost of rollout. Data needed: land ownership, traffic and transport data etc.

As the Department of Digital, Culture, Media and Sport identified earlier this year, “there is currently no single platform capable of ingesting the many and varied datasets required to deliver a comprehensive platform suitable for understanding mmWave propagation for 5G”.

SenSat’s proprietary technologies are solving complex physical geospatial problems like this.

In the case of 5G: SenSat can use simulated realities of major urban areas to extract actionable insights using machine learning techniques; we combine multiple datasets (land use, land ownership, data use) rooted in physical urban geometry, to generate intelligence that can optimise the location and reduce the number of small cells for installation. This reduces the rollout cost for telcos of 5G, and ultimately, the cost of 5G for consumers.

James Dean, CEO and Founder of SenSat

We've also highlighted the best business phones

James Dean is the CEO of SenSat and is a technology entrepreneur fascinated by the way the world works. He founded SenSat to enable the greatest step change since the internet; the digitisation of the physical world.

Samsung Galaxy XCover 7 Rugged Phone review

Asus RT-AX57 Go Review

Quordle today – hints and answers for Monday, April 22 (game #819)

AI for Good blog

Ai and machine learning transforming 5g telecommunications.

Artificial Intelligence
Innovation & Creativity
Machine Learning
25 November 2022

By Miya Nishio

The field of Artificial Intelligence (AI) has been around since the 1950s, when Alan Turing first posed the revolutionary question, “Can machines think?” in his paper Computing Machinery and Intelligence.

However, it is only recently with the exponential growth in data and computing power that have we begun to unlock the full potential of AI. Today, we find that AI is becoming indispensable in various study fields: Health, Climate Change, Robotics, Digital Transformation and as they say, “the sky is the limit.”

One of the fields where the International Telecommunication Union (ITU) is exploring AI and machine learning (ML) solutions is in the application to telecommunication networks, especially around the global deployment of 5G services.

Since 2020, ITU has picked up on the very likely opportunities to discover the usage of AI in 5G, and has been hosting the ITU AI/ML in 5G Challenge . The Challenge is an annual competition where organisations, universities and companies from all across the world provide “problem statements” (a.k.a puzzles or challenges) for teams of professionals and students to solve. This year, 13 problem statements have been proposed by a variety of organizations.

Throughout 2022, ITU has provided open round-table discussions on the AI for Good Neural Network, where participants could directly ask questions to problem statement hosts; webinar sessions held by hosts to explain their problem statement; and additional introductory tutorial sessions to beginners in communication networks and machine learning were offered. In the four-month competition phase, over 500 new participants from 64 countries registered and participated in the challenge.

The 3 promises of 5G – and what do they mean?

According to MIT Technology Review , “5G is a technological paradigm shift, akin to the shift from typewriter to computer.” 5G is accompanied by three broad promises: enhanced mobile broadband, ultra reliable and low latency communications, and massive machine type communications. However, these goals can only become a reality with the help of AI’s ability to deal with big data and the ability to automate systems.

This year ITU kicked off its first session, “ Introduction to communication networks ,” where the basics of communication networks were explained. If you don’t know what is meant by “ enhanced mobile broadband, ultra reliable and low latency communications, and massive machine type communications ” then the video is perfect for you!

These three promises are actually mentioned in recommendation ITU-R M.2083-0 , and are considered as the three main usage scenarios for IMT-2020. IMT-2020 is a standard issued by ITU-R, which envisions the requirements necessary for 5G networks, devices and services.

Going a bit further, ITU’s second session was titled “ ML5G Challenge tutorial #2: Data Pre-processing”. This session was all about data pre-processing, one of the most important steps in Machine Learning, and probably one of the most difficult steps for teams to master.

Again, data pre-processing is a VITAL step to machine learning and can make quite a difference to the end results of your machine learning model. As a result, it is recommended that newcomers to Machine Learning spend a good amount of time practicing this crucial step.

The third session was titled “ ML5G Challenge tutorial #3: Machine Learning model building and training”. This session was about the actual machine learning building and training section of the Machine Learning process.

The fourth session was titled “ ML5G Challenge tutorial #4: Machine Learning Model Optimization and Compression”. This session focused on a much more complicated section of machine learning; optimization and model compression. Grid search was discussed in relation to optimization whilst quantization and pruning techniques were discussed for model compression. While not the most common of topics, we have had several problem statements in the past that dealt with model compression (this is all explained in the video!).

All videos come with a short 15 minute explanation on the topic, and features a jupyter notebook hands-on session, which you can follow and do on your own. You can find all required data, powerpoint slides, and code on the github portal here.

Using data from previous problem statements, if you are interested in possibly joining the AI in 5G Challenge, here is a good place to get start getting comfortable with the types of data you will be using.

While these tutoring webinars cover a great deal on each topic, they only just touch the tip of the iceberg. If you are serious about becoming a machine learning expert, here are some websites you can use to get started to teach yourself the basics:

Code Academy

From these sites, you can review over nearly all of the necessary techniques and methods for Machine Learning, and come back to the explainers when starting a new project!

Awarding top solutions in the Machine Learning Challenge

We will be having our final conference for the 2022 ITU AI/ML in 5G Challenge from November 29 th to December 1 st , where the top teams of each problem statement will present their final solutions to the judges. It is a fantastic opportunity for anyone wanting to learn more about communication networks or machine learning, as it is a chance to see how AI can be applied in one of the most current, fast paced topics: 5G.

Registration is required, so please make sure to sign up here: https://aiforgood.itu.int/event/2022-itu-ai-ml-in-5g-challenge-programme/

Don’t miss the Grand Challenge Finale, where we will award the top winners of the 2022 Competition on the 14 th of December, 2022. Please join us here

And share the event on LinkedIn. https://www.linkedin.com/events/machinelearningin5ggrandefinale6998956031334588416

What impact is AI having on higher education?

AI technologies pioneering societal and artistic advances

The future of socially-assistive robots for good

AI for Good Global Summit

Please fill in the form below

Registrations open until 31 July

Organization*

Organization country* --- Afghanistan Albania Algeria Andorra Angola Antigua and Barbuda Argentina Armenia Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bhutan Bolivia Bosnia and Herzegovina Botswana Brazil Brunei Darussalam Bulgaria Burkina Faso Burundi Cambodia Cameroon Cabo Verde Canada Central African Republic Chad Chile China Colombia Comoros Congo Costa Rica Cote d'Ivoire Croatia Cuba Cyprus Czech Republic Denmark Djibouti Dominica Dominican Republic Ecuador Egypt El Salvador Equatorial Guinea Eritrea Estonia Eswatini Ethiopia Falkland Islands (Malvinas) Faroe Islands Fiji Finland France Gabon Gambia Georgia Germany Ghana Greece Grenada Guatemala Guinea Guinea-Bissau Guyana Haiti Honduras Hungary Iceland India Indonesia Iran Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Kiribati Korea Kuwait Kyrgyzstan Lao People's Democratic Republic Latvia Lebanon Lesotho Liberia Libya Liechtenstein Lithuania Luxembourg Madagascar Malawi Malaysia Maldives Mali Malta Marshall Islands Mauritania Mauritius Micronesia Moldova Monaco Mongolia Montenegro Morocco Mozambique Myanmar Namibia Nauru Nepal Netherlands New Zealand Nicaragua Niger Nigeria North Macedonia Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Qatar Romania Russian Federation Rwanda Saint Kitts and Nevis Saint Lucia Saint Vincent and the Grenadines Samoa San Marino Sao Tome and Principe Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore Slovakia Slovenia Solomon Islands Somalia South Africa South Sudan Spain Sri Lanka Sudan Suriname Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan Tuvalu Uganda Ukraine United Arab Emirates United Kindom United States Uruguay Uzbekistan Vanuatu Venezuela Viet Nam Yemen Zambia Zimbabwe

Email Address*

Confirm Email Address*

First Name*

I do not wish to receive any additional invitations to AI for Good sessions in the future

For important information regarding the classification, please go to the Division’s website and review the last two questions in the Q&A page. Please be advised that the utilization of this list by AI for Good is exclusively for the purpose of ticketing for the 2024 AI for Good Global Summit, unless otherwise specified

Application Deadline: 1 November 2024

Application Deadline: 1 June 2024

Company / Organization*

Company Country* --- Afghanistan Albania Algeria Andorra Angola Antigua and Barbuda Argentina Armenia Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bhutan Bolivia Bosnia and Herzegovina Botswana Brazil Brunei Darussalam Bulgaria Burkina Faso Burundi Cambodia Cameroon Cabo Verde Canada Central African Republic Chad Chile China Colombia Comoros Congo Costa Rica Cote d'Ivoire Croatia Cuba Cyprus Czech Republic Denmark Djibouti Dominica Dominican Republic Ecuador Egypt El Salvador Equatorial Guinea Eritrea Estonia Eswatini Ethiopia Falkland Islands (Malvinas) Faroe Islands Fiji Finland France Gabon Gambia Georgia Germany Ghana Greece Grenada Guatemala Guinea Guinea-Bissau Guyana Haiti Honduras Hungary Iceland India Indonesia Iran Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Kiribati Korea Kuwait Kyrgyzstan Lao People's Democratic Republic Latvia Lebanon Lesotho Liberia Libya Liechtenstein Lithuania Luxembourg Madagascar Malawi Malaysia Maldives Mali Malta Marshall Islands Mauritania Mauritius Micronesia Moldova Monaco Mongolia Montenegro Morocco Mozambique Myanmar Namibia Nauru Nepal Netherlands New Zealand Nicaragua Niger Nigeria North Macedonia Norway Oman Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Qatar Romania Russian Federation Rwanda Saint Kitts and Nevis Saint Lucia Saint Vincent and the Grenadines Samoa San Marino Sao Tome and Principe Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore Slovakia Slovenia Solomon Islands Somalia South Africa South Sudan Spain Sri Lanka Sudan Suriname Sweden Switzerland Syrian Arab Republic Tajikistan Tanzania Thailand Timor-Leste Togo Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan Tuvalu Uganda Ukraine United Arab Emirates United Kindom United States Uruguay Uzbekistan Vanuatu Venezuela Viet Nam Yemen Zambia Zimbabwe

Your pitch deck (file size max. 15MB)*

Solution/Product description (one-liner)*

What is your solution and what is the value proposition (uniqueness) of the solution/product?*

Describe clearly the use/role of Artificial Intelligence in your idea?*

Which UN Sustainable Development Goal(s) does your idea aim to achieve?*

Select the UN Sustainable Development Goal(s) relevant to your project/speaking proposal* GOAL 1: No Poverty GOAL 2: Zero Hunger GOAL 3: Good Health and Well-being GOAL 4: Quality Education GOAL 5: Gender Equality GOAL 6: Clean Water and Sanitation GOAL 7: Affordable and Clean Energy GOAL 8: Decent Work and Economic Growth GOAL 9: Industry, Innovation and Infrastructure GOAL 10: Reduced Inequality GOAL 11: Sustainable Cities and Communities GOAL 12: Responsible Consumption and Production GOAL 13: Climate Action GOAL 14: Life Below Water GOAL 15: Life on Land GOAL 16: Peace and Justice Strong Institutions GOAL 17: Partnerships to achieve the Goal

The registration to attend online will open shortly

The registration to attend in person will open shortly.

By creating an account you agree to our terms and conditions and our privacy policy.

Are you sure you want to remove this speaker?

Voice of the Engineer

Five technologies for building 5G

5G is widely considered a mobile technology that won’t be available until perhaps 2020 or 2021, and even then, not widely. But, as mobile data traffic continues to grow (18-fold over the past 5 years), we’re marching towards the need for 5G speed quicker than ever. Cisco predicts that by 2021, a 5G connection will generate 4.7 times more traffic than the average 4G connection. Figure 1 shows that growth.

5G will be a quantum leap from today’s LTE-Advanced networks. Therefore, it’s necessary to examine five key areas that will help lead the migration from 4G to 5G. Four of the five areas make this transition through an intermediate step called LTE-Advanced Pro (4.5G), which makes the revolution more of an evolution.

Speeds and Feeds This area is the one where the access technology increases from 1 Gbps in LTE-Advanced to 20 Gbps throughput/downlink speed to each cell in 5G. Getting to that speed requires multiple steps starting with LTE-Advanced Pro, which is defined in the specs today and can scale to 3 Gbps using a combination of carrier aggregations (up to 32 carriers), massive MIMO (multiple input, multiple output) of up to 16 antennas, and higher modulation schemes such as 256 QAM ( Figure 2 ).

Data rates of 3 Gbps is achievable without overhauling radio technologies. This is an intermediate step that every operator must take to leverage their current infrastructure and prepare for 5G.

Utilizing the unlicensed spectrum LTE in unlicensed frequencies ( LTE-U ) is already being deployed now by several major carriers including T-Mobile and Verizon, while AT&T is actively pursuing virtual-machine solutions to the issue.

To achieve the higher throughput requirements, the licensed carrier spectrum is not enough. Wi-Fi, the distant cousin to cellular, has been using the unlicensed spectrum for years.

I’m referring to Wi-Fi as the “distant cousin” because it does very similar things to the licensed carrier spectrum, except it’s not regulated. Wi-Fi is free, and as such, quality was largely a non-issue—until recently. Operators have begun to roll out hotspots to offload the cellular traffic wherever possible to Wi-Fi, placing an extra strain on the networks. But, Wi-Fi has a lot of unlicensed spectrum that could be tapped into by LTE.

Because end customers and operators weren’t overly worried about the quality of this “free service,” it was typically fine in residential settings. This mentality has shifted in the last couple of years because of improved techniques in Wi-Fi technologies to attain better quality and regulated access:

Usage of polar codes such as LDPC for error correction.
Higher QAM, meaning Wi-Fi can currently do 256 QAM and approaching 1024 QAM.
4×4 MIMO and Multi-user MIMO to increase throughput and work with more users simultaneously.

Extending the concept of carrier aggregation to unlicensed carriers, which is the same spectrum used in Wi-Fi, will deliver more options for operators to increase bandwidth to a cell.

With the vast amount of spectrum in unlicensed bands today, and what will be released, 5G networks will need to tap into this space to get to ultra-high speed access requirements in addition to finding ways to offload billions of IoT devices.

IoT devices IoT devices pose a diverse set of requirements and challenges.

There is no question that the sheer volume of devices will pose a huge challenge to 5G networks.
IoT devices, unlike traditional cellular devices, are very sporadic in nature. Many of them “sleep” for long periods of time before sending just a few bytes of data. A 5G network needs to plan for infrequent, yet important, communication from these devices.
IoT devices also open a wide variety of security threats. Many of these devices can be used to spread malware or other security attacks to the network.

Handling IoT devices at the same time as regular cellular devices such as smartphones is a daunting task for access and core networks. Starting this with LTE networks now will enable a smoother transition to 5G networks when they arrive. For example, Nokia , Sprint and Verizon are just a few of the big names that began testing 5G this year, although many other carriers claim that they’ll start testing 5G networks “soon.”

Virtualization: NFV & SDN The benefits of virtualization in terms of cost savings for operators, handling elastic demands of a network, and increasing choices for operators, is very clear. 5G networks, due to the extreme needs at both ends of the gamut, which includes sending a few bytes on an infrequent basis, as well as a massive increase in data for a different use case, creates a strong need, and tie, to virtualization of network functions (NFV, Figure 3 ).

Many operators are well on their way to virtualizing their network—especially the packet core. The packet core side is all Internet Protocol (IP) based. This means that much like how data centers were virtualized, the packet core side of wireless (from 3NodeB to the internet) can all be virtualized.

Even on the access side, the split between lower layers of protocols as to what stays in the edge and what moves into the central core is currently being discussed and decided by industry decision makers at network equipment manufacturers (NEMs).

The move to virtualizing and slicing networks to suit the needs of different user devices is starting now, and will gain momentum as 5G networks are deployed.

NR: new radio The 5G-NR has not yet been standardized, and will require a new radio access technology that will increase speeds to 20 Gbps. It requires new millimeter wave (mmWave) radios, which is the band of spectrum between 30 gigahertz (GHz) and 300 GHz that can send/receive data over the air at very high speeds. Per cell bandwidth is expected to be between 10-20 Gbps, with each user potentially able to get 1 Gbps. Things like high-end augmented reality/virtual reality applications need that kind of bandwidth.

5G-NR is the one area that is true 5G. The other four areas below have strong starting points in LTE-Advanced Pro specs and are, as a result, more evolutionary.

There has been a concerted industry push to publish the specifications of 5G that the 3rd Generation Partnership Project (3GPP), met in Dubrovnik , Croatia after the 2017 Mobile World Congress, and advanced the date for release of a portion of the specification to the end of 2017 as opposed to June 2018.

The two big things that are being discussed in 5G-NR are: Supporting a flexible underlying OFDM technology and support for massive MIMO that would enable using mmwave spectrums.

This flexible underlying OFDM technology can enable a multitude of services such as a high broadband video application along with a low-latency, mission-critical application at the same time to a different user in that same cell. Technologies such as scalable numerology based OFDM and scalable An overview of the LTE physical layer—Part III transmission time intervals (TTI)—tie interval in which a user gets data—are being discussed by 3GPP.

While the massive move to 5G is still in early stages, the areas described above will be major steps is leading us to that migration.

Kalyan Sundhar is vice president of mobility, virtualization & applications product for Ixia Solutions Group.

5G front haul, backhaul architectures present wireline test challenges
LTE-Advanced Pro: The bridge to 5G
IMS: Phased-array antennas and beamforming
IMS 2017: 10 Things Not 5G
3GPP Moves 5G Forward in Dubrovnik
OFDM Uncovered Part 1: The Architecture
OFDM Uncovered Part 2: Design Challenges
Fourth Generation Wireless Technology

div-gpt-ad-inread

0 comments on “ Five technologies for building 5G ”

Six operational challenges for 5G

Carriers face a number of operational challenges as 5G continues to evolve and mature, both in terms of deploying and running new 5G networks as well as in managing the relationship of those new networks to existing 4G networks. In a Test and Measurement Forum session , three industry experts laid out some of the primary operational challenges for 5G networks. These include:

– Scale. Dean Brauer, VP of network and field operations for Verizon, said that one of the biggest challenges for network operators is that as 5G is being deployed, the number and types of network nodes are “growing exponentially,” so the ability to both deploy and to manage that network at-scale is crucial.

– Speed of deployment. Not only is 5G being deployed on a broad scale, but it’s being deployed on an aggressive timeline; those represent operational challenges on their own, but they also compound each other. “Service providers really want to deploy a lot of the network very, very fast—so that’s definitely a huge challenge,” says Sophie Legault, director for the transport and datacom businessof test company EXFO.

– Integration . This ties directly to deployment, Brauer notes, in terms of how operators make sure that 5G networks work well with existing 4G networks—which is particularly important because 5G NonStandalone networks depend directly upon LTE as an anchor to carry control plane traffic.

– Testing and support of new 5G technologies. Brauer considers testing to be its own operational challenge in terms of how operators make sure that they can meet the requirements that customers want to see in various 5G services. “We have to be able to measure and monitor the network very well, real-time, at a scale that we haven’t seen before,” he says. Legault frames this in terms of not only new services and related demands, but also new spectrum bands and technological implementations, such as synchronization and high-speed transport.

– Workforce skills. This ties to the new service and technology demands of 5G, but also to the current workforce environment in which 5G is being deployed—a tight labor market, which is also dealing with supply chain issues and where heightened attention and federal/state funding to connectivity services during the global Covid-19 pandemic has meant an influx of dollars that must be spent within a certain time period, so that network operators, system integrators and sub-contractors are competing heatedly for a limited workforce, while also seeking to keep deployment costs down. More is being asked of site technicians: Not just experience with RF testing, but also the ability to test fiber and validate services as well. Sandeep Sharma, VP and global head of Tech Mahindra’s 5G/RAN/ORAN portfolio, also points out that the workforce challenges extend well beyond site deployment. With the increase in virtualization and cloud as well as the emerging role of edge computing in 5G, that changes the set of software skills and familiarity that is needed across the telco industry, Sharma said.

– Network complexity. Sharma went on to add that managing network complexity is one of the most significant operational challenges in 5G. This is compounded by the relatively rapid shift not just from 4G to 5G, but from 5G NonStandalone to Standalone. While the move to 5G SA provides some end-to-end simplification for network management, it doesn’t solve everything—Sharma pointed out that if you look at telco network based on their end-users, the type of 5G devices available, which bands they support, and 5G device penetration still represent issues. There is likely to be a continued lack of continuity across devices, even if there is more continuity across the network. In addition, he said, just as in LTE, the enabling of voice services over the new network (in this case, VoNR) is lagging the support for data services.

Brauer also sees both simplification and increased complexity at work in the move to 5G SA. 5G Standalone, Brauer explains, “will simplify network operation because when you get to a Standalone environment, you no longer have to anchor to your 4G network. So from a performance perspective, and an operational perspective, you get some simplification in how you can operate your network. But the complication comes in because as you introduce 5G, we’re not just introducing a new frequency band … and with Standalone, you can start to slice your network. So the complexity in the operations of the network and the maintenance of the network, and meeting SLAs for various customers goes up exponentially from what we’re used to in our 4G and our 3G networks.”

Legault added, “The network is more dynamic now … and more scalable. So from an operations perspective, you have to be able to understand that and adapt with that change.”

Watch the entirety of this archived session from Test and Measurement Forum, as well as additional forum sessions, on YouTube .

5G Fundamentals
network operations
Tech Mahindra

ABOUT AUTHOR

Jio reaches 108 million 5g subscribers in india, proximus signs deal with nrb to acquire additional 5g spectrum, malaysia’s dnb denies lack of transparency in 5g contracts.

Since 1982, RCR Wireless News has been providing wireless and mobile industry news, insights, and analysis to mobile and wireless industry professionals, decision makers, policy makers, analysts and investors.

About RCR Wireless News
Wireless News Archive

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Publications
Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

Advanced Search
Journal List
Sensors (Basel)

Enhancing 5G Small Cell Selection: A Neural Network and IoV-Based Approach

Ibtihal ahmed alablani.

1 Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia; as.ude.usk@hafara

2 Department of Computer Technology, Technical College, Technical and Vocational Training Corporation, Riyadh 11472, Saudi Arabia

Mohammed Amer Arafah

The ultra-dense network (UDN) is one of the key technologies in fifth generation (5G) networks. It is used to enhance the system capacity issue by deploying small cells at high density. In 5G UDNs, the cell selection process requires high computational complexity, so it is considered to be an open NP-hard problem. Internet of Vehicles (IoV) technology has become a new trend that aims to connect vehicles, people, infrastructure and networks to improve a transportation system. In this paper, we propose a machine-learning and IoV-based cell selection scheme called Artificial Neural Network Cell Selection (ANN-CS). It aims to select the small cell that has the longest dwell time. A feed-forward back-propagation ANN (FFBP-ANN) was trained to perform the selection task, based on moving vehicle information. Real datasets of vehicles and base stations (BSs), collected in Los Angeles, were used for training and evaluation purposes. Simulation results show that the trained ANN model has high accuracy, with a very low percentage of errors. In addition, the proposed ANN-CS decreases the handover rate by up to 33.33% and increases the dwell time by up to 15.47%, thereby minimizing the number of unsuccessful and unnecessary handovers (HOs). Furthermore, it led to an enhancement in terms of the downlink throughput achieved by vehicles.

1. Introduction

The 5G wireless network is a future technology which requires huge capacity, high reliability, massive connectivity, and ultra-low latency [ 1 , 2 ]. The Internet of Things (IoT), smart cities, intelligent transportation, and remote surgery are examples of emerging 5G applications [ 3 , 4 , 5 , 6 ]. Internet of Vehicles (IoV) is a special form of the IoT where vehicles are connected to the internet and they can transmit data [ 7 , 8 ]. The IoV communication has four different types, which are Vehicle-to-Vehicle (V2V) communication, Vehicle-to-Pedestrian (V2P) communication, Vehicle-to-Infrastructure (V2I) and Vehicle-to-Network (V2N) communication [ 9 , 10 ]. Intelligent transportation system (ITS) refers to a smart system that aims to enhance mobility and safety issues by integrating information and communication technologies into the transportation field [ 11 , 12 ]. The IoV will play a significant role in the future intelligent transportation system [ 13 ].

An ultra-dense network is an enabling technology, which aims to meet the requirements of increased capacity and low latency [ 14 ]. It is a wireless network that has a high density of small cells that may exceed the number of active users [ 15 ]. However, UDNs have many challenges to be overcome, as illustrated in Figure 1 . The main issues related to 5G UDNs are cell selection, radio resource allocation, interference mitigation, and power management [ 16 , 17 , 18 ]. Cell selection is the process of determining the small serving BS to which a mobile terminal will associate [ 19 , 20 ]. It is an NP-hard optimization problem and the computational complexity increases exponentially with increasing network size [ 14 , 21 ]. High data rates and the efficient use of a spectrum are crucial requirements for IoT-based 5G networks [ 22 ]. Maximizing the 5G data rate should be targeted so that the IoT transmission rate constraints and interference to IoT are considered. In addition, improving the energy efficiency by consuming less power is essential to meet communication requirements [ 23 ].

An external file that holds a picture, illustration, etc.
Object name is sensors-21-06361-g001.jpg

Main issues related to 5G ultra-dense networks.

Nowadays, machine learning (ML) is becoming a promising method that can offer fast processing and real-time predictions for complex and large-scale applications by developing models and algorithms [ 24 , 25 , 26 ]. An artificial neural network (ANN) is a machine learning algorithm that is based on processing elements (called neurons) to simulate the concept of human neurons [ 27 ]. ANNs have proven their effectiveness in solving many problems in different fields [ 28 ]. Fifth generation (5G) networks require the application of machine learning techniques to operate effectively. Solving issues related to 5G wireless technology is an open direction for future research [ 29 ].

In this paper, we study the cell selection issue in 5G UDNs. A novel cell selection strategy is proposed that is based on ANN to perform the multi-classification task of small BSs, based on vehicle information. The main determinant in choosing a cell is the dwell time spent inside the cell. In the experiment, actual datasets are used for training and evaluation that were gathered in the city of Los Angeles.

The traditional scheme and most existing works give high priority to the small BSs that have the maximum received signal strength indicator (RSSI). However, relying on this principle is not effective in ultra-dense environments because it will lead to an increased handover rate [ 16 , 30 , 31 ]. In addition, machine learning techniques are needed to speed up processing time and to reduce computational complexity.

The main contributions of this work are:

proposing an intelligent ANN-based cell selection strategy for 5G UDNs, called ANN-CS. It aims to select a small BS that has the longest dwell time in the range, using a ML technique. A feed-forward back-propagation ANN (FFBP-ANN) was trained based on real BS and vehicle datasets that were collected in the city of Los Angeles;
evaluating the performance of the trained FFBP-ANN in terms of accuracy, sensitivity, specificity, precision, F-score, and geometric mean (G-mean). In addition, errors are checked based on the root mean square error (RMSE) and the mean absolute error (MAE);
evaluating the performance of the proposed ANN-CS scheme based on the following performance metrics: the average (i) dwell time; (ii) number of handovers; (iii) number of unsuccessful and unnecessary handovers; and (iv) achievable downlink throughput. Then, the performance of the proposed ANN-CS approach is compared with the traditional cell selection method and a recent related approach called Handover based on Residence Time Prediction (HO RTP).

The rest of this paper is structured as follows. Section 2 presents related ML-based cell selection works. The proposed machine-learning-based approach is explained in Section 3 . The simulation results are discussed in Section 4 . The conclusion of the whole paper and suggestions for future work are given in Section 5 . Appendix A gives lists of all abbreviations and symbols that are mentioned in this paper.

2. Related Work

In this section, recent related user association methods are discussed. Some of these works use machine learning (ML) techniques to solve the cell selection issue, while others do not.

2.1. Non ML-Based Cell Selection Strategies

A cell selection approach was proposed by Kiishida et al. in [ 32 ] for 5G multi-layered Radio Access Networks (RANs). It considers the direction and velocity of UE movement to reduce the number of frequent handovers. The final decision is based on the value of SINR, whereby the BS that has the maximum SINR value will be selected. Simulation results proved that the proposed approach achieved an approximate 30% improvement in the number of handovers while maintaining the average flow time.

In [ 33 ], Elkourdi et al. proposed a cell selection algorithm for 5G heterogeneous networks that based on Bayesian game. There are two players, that is, user equipment (UEs) and access nodes (AN). There are different types of UEs based on the traffic. Simulation results showed that the proposed scheme outperformed the traditional and cell-range-expansion (CRE) methods in terms of the probability of proper connection and end-to-end delay.

Waheidi et al. developed an approach called Cell Association, based on a Multi-Armed Bandit game (CA-MAB) in [ 30 ]. There are two classes of devices, that is, UE and IoT, and the proposed CA-MAB scheme was evaluated in static and mobile environments. The evaluation results showed that the CA-MAB approach enhance the energy efficiency and the throughput and the existence of mobility affected the energy savings, throughput, and equilibrium.

Arshad et al. proposed topology-aware skipping approaches in [ 34 ], where various skipping techniques are considered. The handover decision is taken based on the position of a user and/or cell size. Simulation results showed that the proposed schemes outperformed the conventional RSSI-based method by up to 47% in terms of the average user throughput.

Two cell selection strategies for HUDNs were proposed by Sun et al. in [ 35 ] that depend on the coordinated multipoint (CoMP) technology. The first scheme is called movement-aware CoMP handover (MACH), which select the cooperation BSs set that has the strongest received signal with a dwell time greater than a specific threshold. The second scheme is known as improved MACH (iMACH), which adds the nearest BS to the MACH’s cooperation BSs set, instead of the BS that has the lowest RSSI value in the set. The handover is triggered based on MACH, when the farthest BS in the set becomes the nearest one. Conversely, in iMACH, the HO is initiated when the nearest BS becomes the farthest one. Simulation results demonstrated that MACH and iMACH strategies enhanced the average achievable throughput. In addition, they improve the coverage probability and handover rate.

Qin et al. introduced a cell selection strategy for 5G ultra-dense networks in [ 36 ]. It is called Handover based on Resident Time Prediction (HO RTP) and it aims to estimate the residence time inside a cell and select the base station that has the strongest RSSI value with a residence period longer than a predefined threshold. Simulation results demonstrated that the HO RTP approach was superior to the traditional method in terms of achievable mean user throughput.

In [ 16 ], Alablani and Arafah introduced an adaptive cell selection approach for 5G Heterogeneous UDNs (HUDNs), called ADA-CS. It aims to select the best BS based on the different features of HUDNs and vehicle movements. It passes through six phases to achieve its goals; namely, configuration, decision-making, filtering, narrowing, selecting, and HO triggering. Simulation results demonstrated that the ADA-CS strategy was superior to the conventional and recent related approaches in terms of the average number of handovers, average achievable downlink data rates and spectral efficiency.

2.2. ML-Based Cell Selection Strategies

In [ 37 ], Dilranjan et al. proposed a BS prediction strategy for 5G wireless networks that uses a Recurrent Neural Network (RNN) classifier. Received Signal Strength (RSS) values are used to train the RNN model. Simulation results showed that the proposed scheme achieved 98% accuracy in predicting the optimal base station to be associated with.

Zhang et al. introduced a machine-learning-based cell selection scheme for drones in wireless networks in [ 38 ]. A conditional random field (CRF) model is used to predict the best serving cells depending on signal-to- interference-plus-noise ratio (SINR) values. Simulation results demonstrated that the proposed CRF-based method yielded 90% accuracy in predicting the best cells and it outperformed two simple heuristic methods.

In [ 39 ], Perez et al. proposed a machine-learning-based framework to solve the user association problem in 5G heterogeneous networks. The Q-learning algorithm was used to achieve the model goal. 3-dimensional feature vectors were used that included the BS identification (BSID) index, downlink (DL) SINR, and the DL cell load. Simulation results showed the superiority of the proposed framework over alternative decision methods.

Zappone et al. introduced a user association method in [ 40 ] that was based on machine learning. A feed-forward artificial neural network (ANN) was trained to perform the optimal user association where the input was the geographical positions of users. The use of the ANN reduced the computational complexity of the assignment procedure compared to conventional methods.

In [ 41 ], a cell selection issue was solved by introducing two hidden Markov-model- (HMM) based ML strategies that were proposed by Balapuwaduge et al. The reliability and availability of network resources were the main targets of the proposed HMM-based ML schemes. Simulation results showed the superiority of the proposed strategies compared with a random cell selection method in terms of channel availability and reliability.

An intelligent machine-learning-based user association for 5G heterogeneous networks was developed in [ 14 ] by Zhang et al. The problem was treated as a supervised learning task and a cross-entropy algorithm was used for labeling the best base station to be associated with. A U-Net convolutional neural network (CNN) was trained to solve the user association problem under the cell load constraint. Channel gain matrices were mapped onto images to be the inputs of CNN, while the user association matrices were the outputs of the ML model. Simulation results demonstrated that the proposed schemes enhanced computation time and network robustness.

Table 1 represents a comparison among recent ML-based cell selection schemes in terms of the ML model used and its inputs. Based on the cell selection works that are represented in this section, we found the following limitations:

The number of cell selection schemes that rely on applying machine learning technologies in predicting the serving BS is small compared with the number of non ML-based works. However, using ML techniques seems to be essential in an environment that has vehicle movement and ultra-high density BSs to decrease the computational complexity of estimating the best BSs;
Few works consider the estimation of the dwell time, which is, in fact, the main determinant in selecting BSs. Moreover, these works did not give the dwelling period a high priority compared to the value of the received signal strength. In addition, the equations used to estimate the dwell time are inaccurate and assume that the user is located at the edge of the cell, which is contrary to reality;
The ML-based works did not give the model enough types of inputs to be able to predict the best BS efficiently.

A comparison among recent ML-based cell selection schemes.

3. Proposed ML-Based Cell Selection Strategy

3.1. problem formulation.

The proposed ML-Based cell selection, the ANN-CS scheme, aims to reduce the handover rate in 5G UDNs by prolonging the dwell time of vehicles within small cells. Millimeter-Wave (mmWave) communication in UDNs has been considered, which operates in a high-frequency band. The association in downlink with single connectivity between small BSs and vehicles is considered. The small BSs located in the Central cluster of Los Angeles are denoted by B small = { B 1 , B 2 , … , B K } . The vehicles, which move with different movement-related information, are represented by V = { V 1 , V 2 , … , V J } . The BS association vector is expressed as A = { a 11 , a 12 , … , a KJ } , where the BS association variable that indicates the connection between small BS k and vehicle j is defined as shown in Equation ( 1 ).

3.2. Proposed Framework

The framework of the proposed ANN-based small cell selection is represented in Figure 2 . The framework is composed of two main components: a 5G ultra-dense environment and an ANN-based agent. In training and testing processes, there is an interaction between the two components. The vehicle-related information, which includes geographical locations, azimuths, and speeds, is entered in the ANN-based agent. The ANN is used to predict the best small BS to be associated with, based on the longest dwell time, by generating BS-association vectors. A converting unit is used to convert the predicted BS association vector to the corresponding BS’s ID.

An external file that holds a picture, illustration, etc.
Object name is sensors-21-06361-g002.jpg

The framework of the proposed ANN-CS scheme.

The pseudocode for the proposed ANN-CS scheme is shown in Algorithm 1.

3.3. 5G Network Model

A 5G ultra-dense network has been modeled in this paper based on real datasets. In the city of Los Angeles, the distribution of small BSs is in three clusters: (a) Burbank, (b) Central, and (c) Long Beach [ 42 ]. The Central cluster is considered due to the high density of the small BSs.

The system model is shown in Figure 3 , where the black crosses represent the distribution of small BSs and the series of green squares shows the locations of vehicles in LA City. There are 621 small base stations and 48,864 vehicles.

An external file that holds a picture, illustration, etc.
Object name is sensors-21-06361-g003.jpg

The system model in the city of Los Angeles.

3.4. Machine Learning Model

This study is based on using a machine learning technique to solve the 5G small cell selection issue. There are three main phases involved in building the proposed ML model, as represented in Figure 4 . These phases are (1) data preparation, (2) ML model training, and (3) ML model evaluation. The raw data consist of two databases; a dataset of small base stations located in the city of Los Angeles [ 42 ], and a dataset of vehicle information, which was collected in LA City [ 43 ].

An external file that holds a picture, illustration, etc.
Object name is sensors-21-06361-g004.jpg

Building phases of the proposed ANN-based model.

3.4.1. Data Preparation

The data preparation phase is composed of three steps:

An external file that holds a picture, illustration, etc.
Object name is sensors-21-06361-g005a.jpg

Snapshots of LA small BSs and vehicles tables after data curation step. ( a ) Snapshot of LA small BSs table. ( b ) Snapshot of LA vehicles table.

Data labeling step: This is a process of tagging LA vehicles data samples to solve a multi-classification problem via supervised learning. It is performed by generating a BS association vector for each vehicle, where 1 is assigned to the small BS that has the longest dwell time and 0 to the other BSs. The dwell time of a vehicle within small cells, T dwell , is estimated as represented in Equation ( 2 ). T dwell = C s = d cos ( θ ) + r 2 − d 2 si n 2 ( θ ) s , (2) where C is the chord of a small cell, which indicates the length of the dwelling distance within the small cell. The vehicle speed and the distance between the vehicle and small BS are identified by s and d, respectively. The angle between the small BS and the direction of the vehicle is represented by θ and r is the radius of the small cell.

Number of training and testing samples.

3.4.2. ML Model Training

A feed-forward back-propagation ANN (FFBP-ANN) is used to achieve the multi-classification task, as shown in Figure 6 . In the proposed FFBP-ANN structure, there are three layers; input, hidden, and output. The input vector has four values related to vehicles; latitude, longitude, azimuth, and speed. The training data set contains 39,091 feature vectors with different vehicle information. The hidden layer is composed of ten neurons, while the output layer includes K neurons to generate the small BSs association vector. Based on the target vector, the errors are estimated to update the weights of the proposed neural network. Table 3 shows the training parameters that were used for training the proposed FFBP-ANN model.

An external file that holds a picture, illustration, etc.
Object name is sensors-21-06361-g006.jpg

Illustration of the proposed neural network architecture.

The training parameters.

3.4.3. ML Model Evaluation

The root mean square error (RMSE) is a common measure, which calculates the error distance between the predicted values. The mean absolute error (MAE) is a measure used to compute the average of the absolute difference between the predicted and the target values. RMSE and MAE are defined as shown in Equations ( 3 ) and ( 4 ), respectively [ 44 ].

where the number of testing samples is denoted by N and the predicted and the target small BSs are represented by y ^ and y, respectively.

To evaluate the performance of the proposed ANN-based model, a confusion matrix is constructed, which is sometimes called a contingency table [ 45 ]. The confusion matrix is an effective tool that reports the numbers of true positive (TP), true negative (TN), false positive (FP), and false negative (FN) [ 46 ]. Based on the constructed confusion matrix, accuracy, sensitivity, specificity, precision, F-score, and geometric mean (G-mean) are calculated as defined in Equations ( 5 )–( 10 ).

3.5. Propagation Channel Model

The parameters used in the propagation channel model, that is, path loss (PL), fading and shadowing, are shown in Table 4 . The PL model used is the 3rd Generation Partnership Project (3GPP) model, which is given by the 3GPP technical report (specification #38.901, version 16.1.0) [ 47 ]. Urban microcell-line-of-sight (UMi-LOS)/street canyon model is considered in this study. In the Central cluster of LA city, streets are flanked by buildings on both sides, resulting in canyon-like environments, and the small BSs are shorter than the buildings. The path loss function, γ ( d ) , is associated with the distance between a small base station and a vehicle, where the distance (d) is measured in meters and the carrier frequency ( f c ) is expressed in GHz. The breakpoint distance is represented by d B P and the height and the effective height of the small BS are denoted by h B and h B ′ , respectively. The height and the effective height of the vehicle are expressed as h V and h V ′ . The velocity of light in free space is represented by c. The Rayleigh fading model is a common model that can represent multipath fading in real-world environments [ 48 , 49 ]. In this work, multipath fading is modeled as Rayleigh fading to represent the LA city environment, which follows the exponential distribution with unit mean. In this paper, frequency-selective fading is not considered because measurements made in [ 50 ] demonstrate that the delay spread is generally small. Moreover, using techniques like orthogonal frequency-division multiplexing (OFDM) or frequency domain equalization limits the effect of the frequency-selectivity in fading [ 51 ]. In addition, small-scale fading at mmWave cellular systems is less severe than that in Long-Term Evolution (LTE) systems when using base station antennas with narrow beams, as the measurement results show [ 52 ]. The log-normal shadowing is included in the propagation model, where σ S F is the shadow-fading standard deviation in decibels (dB).

The parameters used in the propagation channel model.

4. Simulation Results and Discussion

In this work, the MATLAB simulator 2021a was the simulation tool used to implement and analyze the performance of the proposed ANN-CS algorithm. A high-performance gaming computer was used to perform the data processing and to evaluate the performance. The specifications of the computer are given in Table 5 .

Gaming computer specifications.

4.1. Evaluation of the Trained ANN Model

Figure 7 illustrates the validation performance chart that shows the relations between the number of epochs and the mean squared error (MSE). In the chart, there is a green circle indicating the training stopping time, which occurs when the validation error reaches its minimum and then increases at epoch 354. The test set and the validation set are represented by green and red lines. For all training, validation and test data, as the number of epochs increases, the MSE value decreases. The best training MSE of the trained network equals 0.00021746, which was obtained at epoch 348, and is very small.

An external file that holds a picture, illustration, etc.
Object name is sensors-21-06361-g007.jpg

The relation between number of epochs and training MSE.

Figure 8 illustrates the relations between the number of epochs and the performance of the training state in terms of gradient, Mu factor, and validation fail. The values of the gradient, Mu factor, and validation check at epoch 354 are 1.0493 × 10 − 6 , 1 × 10 − 9 and 6, respectively.

An external file that holds a picture, illustration, etc.
Object name is sensors-21-06361-g008.jpg

The relations between the number of epochs and the performance of the training state.

The trained ANN-based model was evaluated based on RMSE, MAE, accuracy, sensitivity, specificity, precision, F-score, and G-mean, as shown in Table 6 .

The trained model evaluation values.

4.2. Evaluation of the Proposed ANN-CS Scheme

4.2.1. performance metrics.

The performance of the proposed ANN-CS strategy is evaluated in terms of:

Average dwell time: The average dwell time of a vehicle in a small cell is estimated according to Equation ( 11 ), where the number of moving vehicles in an ultra-dense network is expressed as J . E ( T dwell ) = ∑ J ( ∑ ( T dwell ) / N H O ) J . (11)
Average number of handovers: The average number of HOs that occurs as vehicles move in the UDN is computed according to Equation ( 12 ). E ( N HO ) = ∑ J N HO J . (12)
Average number of unsuccessful HO: An unsuccessful HO occurs when the handover latency is longer than the dwell time within a small cell ( l i ) [ 53 ]. The probability of an unsuccessful HO ( P r α ) can be calculated in terms of vehicle speed (s), small cell radius (r), handover latency (l), and the time threshold of an unsuccessful HO ( T h α ), as shown in Equation ( 13 ). Equation ( 15 ) shows the formula to estimate the average number of unsuccessful HOs ( E ( N α ) ). P r ( α ) = 2 π [ arcsin ( s l i 2 r ) − arcsin ( s T h α 2 r ) ] 0 ⩽ T h α ⩽ l i 0 l i < T h α (13) T h α = 2 r s sin ( arcsin ( s l i 2 r ) − 2 π P r ( α ) ) ; 0 ⩽ P r ( α ) ⩽ 1 (14) E ( N α ) = P r ( α ) × E ( N H O ) . (15)
Average number of unnecessary HOs: An unnecessary HO means a false handover is performed, where the dwell time in a small cell is shorter than the summation of HO latencies to move into ( l i ) and out ( l o ) of the small cell [ 54 ]. The probability of an unnecessary HO ( P r ( β )) can be calculated as expressed in Equation ( 16 ). The time threshold of the unnecessary handover is denoted by T h β . Equation ( 18 ) illustrates the method of computing the average number of unnecessary HOs ( E ( N β ) ). P r ( β ) = 2 π [ arcsin ( s ( l i + l o ) 2 r ) − arcsin ( s T h β 2 r ) ] 0 ⩽ T h β ⩽ ( l i + l o ) 0 ( l i + l o ) < T h β (16) T h β = 2 r s sin ( arcsin ( s ( l i + l o ) 2 r ) − 2 π P r ( β ) ) ; 0 ⩽ P r ( β ) ⩽ 1 (17) E ( N β ) = P r ( β ) × E ( N H O ) . (18)

The maximum transmission power of small BSs is denoted as p t x and the path loss function is represented by γ ( d ) , which is defined in Section 3.5 . The channel gain is expressed as H, which includes the effects of Rayleigh fading and log-normal shadowing. The thermal noise ( σ 2 ) is modeled as an additive white Gaussian noise (AWGN), as shown in Equation ( 21 ). It can be computed in terms of noise power spectral density ( N 0 ), and sub-channel bandwidth (W).

A throughput is the sum of effective achievable data rate over the network during movement [ 35 ]. The throughput of a vehicle can be calculated based on Equation ( 22 ).

4.2.2. Performance Results

In this section, we compare the performance of our proposed ANN-CS approach with the traditional and HO RTP cell selection schemes. The simulation parameters that are used in this work are listed in Table 7 .

Simulation parameters.

Figure 9 and Figure 10 represent the average dwell time and average number of handovers under different moving speeds. Increasing the speed will reduce the average dwell time of vehicles inside small cells and, therefore, increase the average number of handovers. The proposed ANN-CS approach prolongs the dwell time by estimating it based on the direction and speed of vehicles in addition to small cell specifications. As the chart indicates, the ANN-CS approach has the longest average dwell time and it is superior to the traditional and HO RTP approaches by 15.47% and 7.56%, respectively. The reason is that the traditional cell selection method chooses the small BS that has the largest RSSI value, even if it does not lie on a vehicle’s trajectory. The ANN-CS strategy outperforms the HO RTP approach because HO RTP estimates the time resident inside the cell but it selects the small BS that has the highest signal strength value with residence time greater than a predefined dwell time threshold. Therefore, the primary criterion for selection is the strength of the received signal. In addition, the ANN-CS approach outperforms the traditional and RTP HO schemes by 33.33% and 18.18% in terms of the average number of handovers.

An external file that holds a picture, illustration, etc.
Object name is sensors-21-06361-g009.jpg

Average dwell time.

An external file that holds a picture, illustration, etc.
Object name is sensors-21-06361-g010.jpg

Average number of HOs.

Figure 11 and Figure 12 represent the average number of unsuccessful and unnecessary handovers against different vehicle speeds. Increasing the speed of vehicles leads to an increase in the probabilities of unsuccessful and unnecessary HOs due to the decrease in the length of the dwelling period inside the small cell. In terms of the average number of unsuccessful and unnecessary handovers, our proposed ANN-CS approach outperforms the traditional and HO RTP selection schemes by 33.55% and 19.04%, respectively.

An external file that holds a picture, illustration, etc.
Object name is sensors-21-06361-g011.jpg

Average number of unsuccessful HOs.

An external file that holds a picture, illustration, etc.
Object name is sensors-21-06361-g012.jpg

Average number of unnecessary HOs.

Figure 13 displays the relationship between the average achievable downlink throughput and vehicle speed. We found that the proposed ANN-CS made improvements over the traditional and HO RTP approaches by 1.2% and 0.1%, respectively. Although the ANN-CS method does not choose the closest small cell, it can achieve enhancements over the methods that give high priority to the received signal strength criteria. This is because the achievable DL throughput is negatively affected by an increase in the number of HOs due to the latency caused by moving from one small cell to another. In addition, the peak data rate is usually reached by our ANN-CS scheme when the vehicle is at the middle of the small cell, while the peak data rates may not be achieved by RSSI-based methods.

An external file that holds a picture, illustration, etc.
Object name is sensors-21-06361-g013.jpg

Average achievable downlink throughput.

5. Conclusions and Future Work

The IoV is a fundamental technology that will improve the transportation system. In ultra-dense networks, cell selection is considered an NP-hard problem. In this paper, we solve the cell selection issue for 5G UDNs by applying a machine learning technique. A neural network and IoV-based algorithm called the ANN-CS scheme is proposed that uses a trained feed-forward back-propagation ANN model to perform the multi-classification task of selecting small base stations. It aims to prolong the dwell time within small cells and thereby decrease the number of handovers. Real datasets are used for training and evaluation purposes, which were collected in the city of Los Angeles. The trained ANN-FFBP model is able to predict the best small BS with high accuracy and a very low error percentage. Simulation results show that our proposed ANN-CS scheme can achieve its goals by decreasing the HOs rate and prolonging the dwell time of vehicles within small cells, and thus the numbers of unsuccessful and unnecessary HOs are minimized. Moreover, the achievable DL throughput is enhanced when using our approach compared with other existing methods. In addition, the computational complexity is reduced by using the ANN, compared with non-ML-based methods. For future work, other machine learning techniques can be applied to solve the cell selection issue in 5G UDNs. A machine learning model can be trained based on different types of input features to make the model applicable to different environments.

Appendix A. Lists of Abbreviations and Symbols

Lists of the abbreviations and symbols that are used in this paper are given in Table A1 and Table A2 , respectively.

List of abbreviations.

List of main symbols.

Author Contributions

I.A.A. collected the data, performed the experiments, analyzed the results, and wrote the paper. M.A.A. supervised the research and critically revised the paper. All authors have read and agreed to the published version of the manuscript.

The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through research group No (RG-1440-122).

Conflicts of Interest

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

COMMENTS

5G: A Japanese Approach to Problem Solving
Published Oct 14, 2023. + Follow. The 5G Method, derived from five Japanese words, offers a structured and holistic way to address and resolve problems. These five "G"s are Gemba, Gembutsu ...
PDF Planning for 5G a problem structuring approach
discuss problem structuring and modelling techniques and their application to our problem. Further, we apply stakeholder mapping and Hierarchical Process Modeling (HPM). In section IV we discuss the implications of our problem structuring. In section V we perform a second iteration of our methodology incorporating our findings so far.
Researchers work to solve 5G network problems when it matters: right
"This was a major breakthrough," said Hou. "With support from the Commonwealth Cyber Initiative in Southwest Virginia, we elevated network optimization to a whole different level: solving problems in the field in real time." Ultra-high precision for ultra-low latency. In the context of 5G, timing is tied up with the concept of latency.
Three Technologies That Can Help Make 5G A Reality
As Cabe Atwell reports in Fierce Electronics, there are three technologies that can help solve this problem. One is microwave anyhaul, which can deliver data without relying on fiber. Using this technology, air interfaces are strapped to towers and can send radio signals up to 170 GHz over expansive regions. A second potential solution is with ...
Why 5G is truly the foundation of successful root cause analysis
Utilizing the "5G Principles" truly took my ability to lead a team during an RCA to another level. The use of 5G in combination with access to a robust root cause analysis tool allowed me to ...
Solving 5G's Thorniest Issues
Solving 5G's Thorniest Issues. Incomplete roadmaps and continued uncertainty about millimeter-wave technology make this technology's future hazy, but solutions are in the works. August 1st, 2019 - By: Ed Sperling. 5G rollouts are beginning to hit the market, accompanied by a long list of unsolved technical and business issues surrounding ...
Structured Problem-Solving
The added advantage is the 5G approach quashes the division in organizations created by opinions as it is focussed on Data & Facts. Problem solving becomes more structured with revision of ...
5G is Finally Here, But Questions and Issues Remain
5G enables fast, secure, and pervasive connectivity across smart networks and Internet of Things (IoT) devices. When combined with artificial intelligence, 5G can enable unparalleled productivity and efficiency. Apart from the global estimate, five industries are poised to see incremental growth: Industrial Manufacturing, Connected Healthcare ...
What to expect from the AI/ML in 5G Challenge
News 1 Dec 2021. The 2nd Artificial Intelligence/Machine Learning (AI/ML) in 5G Challenge is set to conclude in December, capping a successful debut last year. Organized by the International Telecommunication Union (ITU) as part of its AI for Good initiative, the competition sees participants around the world solving real-world problems by ...
Solving for 5G: How Math Modeling Can Improve Modern Communications
Solving for 5G: How Math Modeling Can Improve Modern Communications. by Sponsored Post June 17, 2020. 5G promises exponential increases in data speeds, but questions linger about how to deploy it in urban environments. Editor's note: This commentary, written by Assistant Professor Eric Stachura, is published as part of an annual sponsorship ...
Resource Allocation Schemes for 5G Network: A Systematic Review
Resource allocation based on artificial intelligence techniques was utilized to resolve issues such as a hybrid problem-solving approach. The main goal behind the study was create a basis on which to select and/or improve a particular technique in the future as a means to allocate the resources of a 5G network.
5G Coverage Problem Analysis and Optimization Scheme Exploration
Quality of coverage is the key to affect the quality and index of mobile communication, so it is very important to solve the problem of coverage. From the perspective of improving the quality of 5G wireless network, this paper studies the common weak coverage and over coverage problems in 5G wireless network, analyzes the causes, the processing ideas and analysis methods in daily centralized ...
Latest Performance Improvement Strategies and Techniques Used in 5G
The problem caused by interference can be solved effectively by a distance protection scheme. ... K Y, and Kz could be extracted by solving the transcendental equation as follows. k x tan (k x a 2) = ... especially in the designing structure of antenna for 5G application. In such techniques, the radiation losses are comparatively lower as in ...
Solving operational issues with 5G CNR
Solving the high band 5G handover puzzle. The Ericsson 5G Centralized Neighbor Relations rApp is an automated solution. It enables the user to efficiently search for 4G to 5G cell neighbor candidates in the network and creates the necessary objects for these neighbor relations automatically. It does this by establishing cell-neighbor relations ...
A Survey on 5G Coverage Improvement Techniques: Issues and Future
5G Optimization—Future Challenges and Research Issues. 5G key parameters such as improved spectral efficiency, low latency, high data rates, and extended coverage capacity are achieved using the optimization algorithms. There are still some challenges and issues in the implementation of AI-based optimization algorithms that need to be addressed.
Next on 5G: Enabling supercharged problem-solving
Next on 5G: Enabling supercharged problem-solving. 5G is much more than just a new carriage network. The 5th generation mobile network is an enabler that will supercharge a wider range of emerging technologies, such as artificial intelligence, just-in-time supply chain management and augmented reality. In fact, far more than just giving us ...
How 5G technologies can be implemented more efficiently
In the case of 5G: SenSat can use simulated realities of major urban areas to extract actionable insights using machine learning techniques; we combine multiple datasets (land use, land ownership ...
AI and machine learning transforming 5G telecommunications
The 3 promises of 5G - and what do they mean? According to MIT Technology Review, "5G is a technological paradigm shift, akin to the shift from typewriter to computer." 5G is accompanied by three broad promises: enhanced mobile broadband, ultra reliable and low latency communications, and massive machine type communications.However, these goals can only become a reality with the help of ...
Five technologies for building 5G
Starting this with LTE networks now will enable a smoother transition to 5G networks when they arrive. For example, Nokia, Sprint and Verizon are just a few of the big names that began testing 5G this year, although many other carriers claim that they'll start testing 5G networks "soon.". Virtualization: NFV & SDN.
Security Challenges In The 5G World And How To Overcome Them
How 5G Changes Communication. With the help of 5G, connections between devices will be faster, substantially more scalable and have much lower latency, albeit not all at the same time. Broadly ...
Six operational challenges for 5G
-Workforce skills. This ties to the new service and technology demands of 5G, but also to the current workforce environment in which 5G is being deployed—a tight labor market, which is also dealing with supply chain issues and where heightened attention and federal/state funding to connectivity services during the global Covid-19 pandemic has meant an influx of dollars that must be spent ...
How to Coordinate 5G Teams: Tips and Best Practices
You should also promote the creativity and problem-solving skills of the teams, and support them to explore new solutions and opportunities. Add your perspective Help others by sharing more (125 ...
Enhancing 5G Small Cell Selection: A Neural Network and IoV-Based
Fifth generation (5G) networks require the application of machine learning techniques to operate effectively. Solving issues related to 5G wireless technology is an open direction for future research . ... Perez et al. proposed a machine-learning-based framework to solve the user association problem in 5G heterogeneous networks. The Q-learning ...