Call for Chapters in Multimedia communication and AI
1st Edition
Driving 5G Mobile communications with Artificial Intelligence towards 6G
Dragorad Milovanovic, Zoran Bojkovic, Tulsi Pawan Fowdur
CRC Press 2022
502 Pages + 150 B/W Illustrations
ISBN13: 9781032071244 (HB)ISBN13: 9781032071275 (PB)ISBN13: 9781003205494 (EB)
Book descriptionFifth-generation (5G) mobile communication is all about new advanced technology. The use of data-driven algorithms is an innovative topic to be covered and of interest to many people in the area of wireless technology. Machine learning (ML) and artificial intelligence (AI) methods have recently proposed new approaches of modeling, designing, optimizing and implementing communication systems. ML is great to get insights about complex networks that use large amounts of data, and for predictive and proactive adaptation to dynamic wireless environments. Machine learning has evolved to the point that this technique enhances communications and become a crucial technology for mobile broadband, low-latency communication and massive IoT. Emerging Beyond-5G wireless communication technologies bring complex optimization problems, complex modeling and low-complexity processing requirements. Significant amount of important solutions have been achieved within the last decade based on research advances in algorithmic efficiency of data-driven approaches. The data-driven network is a novel service paradigm that offers a new application for the future of 6G wireless communication and network architecture. The increasing application of ML in multimedia communication is motivated by large amounts of unexploited data and inherent complexity of novel use cases such as extended reality (XR) and high-quality video communications. These use cases vary widely in mobility requirements, the numbers of implicated devices, and bandwidth and latency requirements. In complex 5G networks, substantial performance gains may result from machine learning’s ability to learn complex patterns and adapt to multiple contexts and domains. As networks become increasingly complex and costly to operate, communications service providers are looking for ways to efficiently manage their networks. Automation harnessing the power of AI is a clear choice. But scalability and flexibility of solutions remain as challenges. The book elaborates use case and optimisation problems partitioned in major areas of network planning (element placement, dimensioning), network diagnostics/insights (forecasting, QoE, SLA prediction, detection), and network optimisation and control (functional split according capacity, resource allocation, E2E slicing, proactive reservation, decision making, KPI validation and system troubleshooting). The goal of the book Driving 5G Mobile Communications with Artificial Intelligence towards 6G is to present the latest advances in the field of machine learning for communications and to further prospect this emerging research field. Chapters from internationally-renowned researchers from academia and industry are in preparation.
Aim and scopeThis book aims to bring together a collection of chapters dealing with technical challenges and recent results in intelligent mobile network deployment and to present novel applications envisioned in the era of 5G in particular and potentially for 6G. In addition to presenting fundamental concepts in 5G communications and Machine learning, the book describes the different levels from applications down to physical layer signal processing, at which AI can be applied to 5G. The rationale is to present to researchers and engineers a well-structured view of the different layers forming part for both the access and non-access stratum of 5G, at which AI and machine learning techniques can be effectively employed. In contrast with previous works in this field, we aim to clearly identify and classify the application of AI at each layer of the 5G mobile communication system and also provide insights for 6G applications. The advent of 5G is introducing new challenges for mobile communications service providers, and integrating artificial intelligence (AI) techniques into networks is revolutionary way the industry is addressing these complexities. AI is already being incorporated into networks, with a primary focus on reducing capital expenditure, optimizing network performance and building new revenue streams. It will be vital for improving customer service and enhancing customer experience. The main aim of this book is to provide a well-structured description on how AI is being applied at the different layers of the 5G network and how it can pave the way to 6G networks.
Engineering teamIn 2020, several mobile companies started deploying the fifth generation mobile communication networks. 5G is expected to satisfy three main application areas namely enhanced Mobile Broadband (eMBB), Ultra Reliable Low Latency Communications (uRLLC), and Massive Machine Type Communications (mMTC). In fact due to the exponentially growing user demands, which has been driven by the fast proliferation of numerous ubiquitous applications, further enhancements are being required from mobile network architectures. Mobile network developers and researchers therefore have to completely rethink their strategies for network development and development to cope with the ever-changing network environment. The 5G mobile network includes list of diverse requirements, standardized specifications, and range of implementation choices. The 5G standardization has been progressing at an astonishingly rapid phase, where 3GPP Release 15 Phase 1 and Release 16 Phase 2 have set the foundations of the 5G system, while Release 17 completing the first phase of the 5G evolution. The primary aim of Release 17 is to improve 5GS performance, support new use cases and verticals, and provide ubiquitous connectivity in different deployment conditions and scenarios. Starting from 3GPP Release 16, to promote network intelligence, continuous advancement has been carried out on the technical standardization of network infrastructure (SA2) and network management (SA5): NWDAF is a standard AI+Big data engine; MDAS (Management Data Analytics System) combines with AI and machine learning and enables automation and cognition of the management and orchestration of networks and services. Release 18 introduces further intelligence into wireless networks by implementing machine-learning-based techniques at different levels of the network. Release 18 sets balanced concepts for evolution to 5G-Advanced, in terms of mobile broadband evolution versus further vertical domain expansion, immediate versus longer-term market needs, and device evolution versus network evolution. 5G longer-term evolution is now entering the 5G-Advanced era, starting with 3GPP Release 18. The preparation for 5G-Advanced was ongoing during 2021, leading to specification work starting in 2022 and reaching completion around the end of 2023 and start of 2024 for the various items.
Aim and scopeThis book aims to bring together a collection of chapters dealing with technical challenges and recent results in intelligent mobile network deployment and to present novel applications envisioned in the era of 5G in particular and potentially for 6G. In addition to presenting fundamental concepts in 5G communications and Machine learning, the book describes the different levels from applications down to physical layer signal processing, at which AI can be applied to 5G. The rationale is to present to researchers and engineers a well-structured view of the different layers forming part for both the access and non-access stratum of 5G, at which AI and machine learning techniques can be effectively employed. In contrast with previous works in this field, we aim to clearly identify and classify the application of AI at each layer of the 5G mobile communication system and also provide insights for 6G applications. The advent of 5G is introducing new challenges for mobile communications service providers, and integrating artificial intelligence (AI) techniques into networks is revolutionary way the industry is addressing these complexities. AI is already being incorporated into networks, with a primary focus on reducing capital expenditure, optimizing network performance and building new revenue streams. It will be vital for improving customer service and enhancing customer experience. The main aim of this book is to provide a well-structured description on how AI is being applied at the different layers of the 5G network and how it can pave the way to 6G networks.
Engineering teamIn 2020, several mobile companies started deploying the fifth generation mobile communication networks. 5G is expected to satisfy three main application areas namely enhanced Mobile Broadband (eMBB), Ultra Reliable Low Latency Communications (uRLLC), and Massive Machine Type Communications (mMTC). In fact due to the exponentially growing user demands, which has been driven by the fast proliferation of numerous ubiquitous applications, further enhancements are being required from mobile network architectures. Mobile network developers and researchers therefore have to completely rethink their strategies for network development and development to cope with the ever-changing network environment. The 5G mobile network includes list of diverse requirements, standardized specifications, and range of implementation choices. The 5G standardization has been progressing at an astonishingly rapid phase, where 3GPP Release 15 Phase 1 and Release 16 Phase 2 have set the foundations of the 5G system, while Release 17 completing the first phase of the 5G evolution. The primary aim of Release 17 is to improve 5GS performance, support new use cases and verticals, and provide ubiquitous connectivity in different deployment conditions and scenarios. Starting from 3GPP Release 16, to promote network intelligence, continuous advancement has been carried out on the technical standardization of network infrastructure (SA2) and network management (SA5): NWDAF is a standard AI+Big data engine; MDAS (Management Data Analytics System) combines with AI and machine learning and enables automation and cognition of the management and orchestration of networks and services. Release 18 introduces further intelligence into wireless networks by implementing machine-learning-based techniques at different levels of the network. Release 18 sets balanced concepts for evolution to 5G-Advanced, in terms of mobile broadband evolution versus further vertical domain expansion, immediate versus longer-term market needs, and device evolution versus network evolution. 5G longer-term evolution is now entering the 5G-Advanced era, starting with 3GPP Release 18. The preparation for 5G-Advanced was ongoing during 2021, leading to specification work starting in 2022 and reaching completion around the end of 2023 and start of 2024 for the various items.
5G-Advanced. Today, with the first phase of 5G standards commercially deployed, the technology continues to evolve and 3GPP officially names as 5G-Advanced on April 27, 2021. 5G-Advanced network will define new goals and capabilities for the 5G evolution to enable the generation of greater social and economic value through network evolution and technological enhancements. 5G-Advanced will introduce more intelligence into wireless networks by including suitable machine-learning-based techniques in different levels of the network. Artificial Intelligence represents network AI, including full use of machine learning, digital twins, recognition and intention network, which can enhance the capabilities of network's intelligent operation and maintenance. Convergence includes 5G and industry network convergence, home network convergence and space-air-ground network convergence, in order to realize the integration development. Enablement provides for the enhancement of 5G interactive communication and deterministic communication capabilities. In the process of end-to-end 5G-Advanced network evolution, the evolution of the core network plays a pivotal role. Future enhancements will also cover a wide variety of new verticals and use cases powered by AI/ML technologies based on a single 5G platform. At the architectural level, the 5G-Advanced network needs to fully consider the concept of cloud-native, edge network, network as a service, and continue to enhance network capabilities and eventually move toward cloud-network integration and computing-network integration. Therefore, many standardization activities remain ongoing, despite the first commercial deployments of 5G networks already underway. Among others Europe is establishing the Joint Undertaking on Smart Networks and Services in the frame of the Horizon Europe programme for research and innovation. Other initiatives are Secure 5G & Beyond Act in the U.S., roadmap towards 6G in Japan, MSIT 6G programme in S. Korea, and MIIT 6G programme in China. In the future, it is perhaps inevitable that users will demand greater global coverage, higher data rates, and ubiquitous availability of new and future Internet services and applications. It is therefore important to adopt new paradigms such as those based on Artificial Intelligence in the design for mobile networks. Artificial intelligence has indeed become the basis of several disruptive technologies such as Autonomous transportation, Industrial IoT, Augmented / Virtual reality and intelligent connectivity. In this respect, Artificial Intelligence along with machine learning algorithms can prove to be very promising when combined with 5G to deploy and satisfy the requirements of these application areas.
Artificial Intelligence. AI can effectively enhance the deployment and development of various aspects in 5G networks. Applying AI to both the 5G network and the device will lead to more efficient wireless communications, longer battery life, and enhanced user experiences. AI is a powerful tool, and the key to harnessing AI to improve wireless is to focus on important wireless challenges that are both difficult to solve with traditional methods and are also a good fit for machine learning. Deep wireless domain knowledge is required to know where to use AI’s capabilities. For example, it can equip 5G networks with intelligent self-organizing capabilities to automate several processes in the network operation. Automated network solutions can study metadata, learn network behaviours, generate predictive analysis and present recommendations. The software can even undertake remedial measures before an issue has occurred. It can support 5G enabled applications such as mobile multimedia, AR/VR, IIoT and autonomous vehicles. Several traffic and network management tasks such as mobility management, scheduling, routing and network slicing can be effectively enhanced using machine learning algorithms. Moreover, improved channel coding, modulation, MIMO and other signal processing and detection mechanisms can also be developed using AI for the 5G-NR. In general, (AI) techniques offer an alternative option that is possibly superior to traditional ones in terms of performance and adaptability.5G drawbacks are prompting worldwide activities focused on defining the next generation 6G wireless system that are in a position to integrate far-reaching applications, starting from autonomous systems to extended reality (XR). New services such as mobile broadband reliable low-latency communication, human-centric service and multi-purpose convergence of communications, computing, control and sensing are planned. In order to enable the new services and their performance some technologies must be integrated into 6G, such as edge AI, transceivers with integrated high-frequency bands, communication with large intelligent surfaces, integrated terrestrial, airborne and satellite networks. In spite of many 6G initiatives, the fundamental architectural components of 6G remain undefined.
Trend. 5G drawbacks are prompting worldwide activities focused on defining the next generation 6G wireless system that are in a position to integrate far-reaching applications, starting from autonomous systems to extended reality (XR). New services such as mobile broadband reliable low latency communication, human-centric service and multi-purpose convergence of communications, computing, control and sensing are planned. In order to enable the new services and their performance some technologies must be integrated into 6G, such as edge AI, transceivers with integrated high-frequency bands, communication with large intelligent surfaces, integrated terrestrial, airborne and satellite networks. In spite of many 6G initiatives, the fundamental architectural components of 6G remain undefined. The drivers of 6G will be past trends such as densification, higher rates, and massive antennas. On the other hand, emerging trends understand new services and wireless devices, for example, artificial intelligence (AI), smart wearables, XR devices, computing and sensing. Radically redefining standard 5G services, the main applications that motivate 6G deployment, technological trends, target performance metrics and new service requirements will be introduced. The central question will remain live multimedia streaming, together with few new application domains: multisensory XR applications, connected robotics and autonomous systems, wireless brain-computing interactions, as well as distributed technologies. All these applications lead to new system-wide trends that will emphasize the goals for 6G. The trends are as follows: more bits, more spectrum, more reliability, from aerial to volumetric spectral and energy efficiency, emergence of smart surfaces and environments, massive availability of small data, from self-organizing networks to self-sustaining networks, convergence of communications, computing, control localization and sensing, and finally, the end of smartphone era.
Artificial Intelligence. AI can effectively enhance the deployment and development of various aspects in 5G networks. Applying AI to both the 5G network and the device will lead to more efficient wireless communications, longer battery life, and enhanced user experiences. AI is a powerful tool, and the key to harnessing AI to improve wireless is to focus on important wireless challenges that are both difficult to solve with traditional methods and are also a good fit for machine learning. Deep wireless domain knowledge is required to know where to use AI’s capabilities. For example, it can equip 5G networks with intelligent self-organizing capabilities to automate several processes in the network operation. Automated network solutions can study metadata, learn network behaviours, generate predictive analysis and present recommendations. The software can even undertake remedial measures before an issue has occurred. It can support 5G enabled applications such as mobile multimedia, AR/VR, IIoT and autonomous vehicles. Several traffic and network management tasks such as mobility management, scheduling, routing and network slicing can be effectively enhanced using machine learning algorithms. Moreover, improved channel coding, modulation, MIMO and other signal processing and detection mechanisms can also be developed using AI for the 5G-NR. In general, (AI) techniques offer an alternative option that is possibly superior to traditional ones in terms of performance and adaptability.5G drawbacks are prompting worldwide activities focused on defining the next generation 6G wireless system that are in a position to integrate far-reaching applications, starting from autonomous systems to extended reality (XR). New services such as mobile broadband reliable low-latency communication, human-centric service and multi-purpose convergence of communications, computing, control and sensing are planned. In order to enable the new services and their performance some technologies must be integrated into 6G, such as edge AI, transceivers with integrated high-frequency bands, communication with large intelligent surfaces, integrated terrestrial, airborne and satellite networks. In spite of many 6G initiatives, the fundamental architectural components of 6G remain undefined.
Trend. 5G drawbacks are prompting worldwide activities focused on defining the next generation 6G wireless system that are in a position to integrate far-reaching applications, starting from autonomous systems to extended reality (XR). New services such as mobile broadband reliable low latency communication, human-centric service and multi-purpose convergence of communications, computing, control and sensing are planned. In order to enable the new services and their performance some technologies must be integrated into 6G, such as edge AI, transceivers with integrated high-frequency bands, communication with large intelligent surfaces, integrated terrestrial, airborne and satellite networks. In spite of many 6G initiatives, the fundamental architectural components of 6G remain undefined. The drivers of 6G will be past trends such as densification, higher rates, and massive antennas. On the other hand, emerging trends understand new services and wireless devices, for example, artificial intelligence (AI), smart wearables, XR devices, computing and sensing. Radically redefining standard 5G services, the main applications that motivate 6G deployment, technological trends, target performance metrics and new service requirements will be introduced. The central question will remain live multimedia streaming, together with few new application domains: multisensory XR applications, connected robotics and autonomous systems, wireless brain-computing interactions, as well as distributed technologies. All these applications lead to new system-wide trends that will emphasize the goals for 6G. The trends are as follows: more bits, more spectrum, more reliability, from aerial to volumetric spectral and energy efficiency, emergence of smart surfaces and environments, massive availability of small data, from self-organizing networks to self-sustaining networks, convergence of communications, computing, control localization and sensing, and finally, the end of smartphone era.
Chapter submission
All interested researchers are requested to submit their Abstract for chapter proposal through editor's email dragoam@gmail.com The proposal should clearly specify the mission, priorities, structure and tentative headings for their proposed Chapter as described below within 1-2 page(s):Chapter submission
- Title
- Name, affiliation & contact details of all the authors
- Abstract (please write a brief summary of the intended chapter)
- Motivation & Objectives (please describe clearly the goals and outcome of the intended chapter, in association with the theme of this Book)
- Framework of the chapter (along with the tentative structure of the proposed chapter, please write a brief description of each of the subsections)
- References
- Short Bio of all the authors
Table of ContentsPART 1. Advanced 5G communication
- 5G Advanced mobile communications: New concepts and research challenges
- 5G Advanced mobile broadband: New multimedia service platform
- 5G Ultra-reliable and low-latency communication in vertical domain expansion
- Vehicular systems for 5G and beyond 5G: Channel modelling for performance evaluation
- Distribution of NFV infrastructure providing efficient Edge computing architecture for 5G environments
- 5G-AIoT artificial intelligence of things: Opportunity and challenges
- Machine Learning based scheduling in 5G/6G communication systems
- Application of Deep Learning techniques to modulation and detection for 5G and beyond wireless systems
- AI based channel coding for 5G/6G communications
- Enabling technologies and applications of 5G/6G powered intelligent connectivity
- AI-assisted Extended Reality towards the 6G era: opportunities, challenges and prospective solutions
- An integrated 5G-IoT architecture in next-generation Smart Grid: standardization and interoperability issues
- Privacy requirements in a hyper-connected world: data innovation vs. data protection
- Evaluation of representative 6G use cases: Identification of functional requirements and technology trends
Important dates
Important dates
- First call for chapter proposal March 2021
- Abstract submission deadline July 2021
- Final acceptance notification Sept. 2021
- Final manuscript deadline July 2022
- Publication date March 2023