5G-Advanced

Wanshi Chen, Xing-Qin Lin, Ju-Ho Lee, A. Toskala, Shu Sun, C. Chiasserini, Ling-Jia Liu

Since the start of 5G work in 3GPP in early 2016, tremendous progress has been made in both standardization and commercial deployments. 3GPP is now entering the second phase of 5G standardization, known as 5G-Advanced, built on the 5G baseline in 3GPP Releases 15, 16, and 17. 3GPP Release 18, the start of 5G-Advanced, includes a diverse set of features that cover both device and network evolutions, providing balanced mobile broadband evolution and further vertical domain expansion and accommodating both immediate and long-term commercial needs. 5G-Advanced will significantly expand 5G capabilities, address many new use cases, transform connectivity experiences, and serve as an essential step in developing mobile communications towards 6G. This paper provides a comprehensive overview of the 3GPP 5G-Advanced development, introducing the prominent state-of-the-art technologies investigated in 3GPP and identifying key evolution directions for future research and standardization.

Hongzhi Guo, Yu-Tao Wang, Jiajia Liu, Chang Liu

In 5G advanced and beyond, latency-critical and computation-intensive applications require more communication and computing resources. However, remote areas without available terrestrial edge/cloud infrastructure fail to satisfy these applications’ demands. This motivates the emergence of the UAV-enabled aerial computing paradigm. Single UAV-enabled aerial computing (SUEAC) is limited by small coverage area and insufficient resources, which cannot meet the application requirements. Multiple UAV-enabled aerial computing (MUEAC) has broken through the limitation of SUEAC and has attracted wide attention. Cooperation among multiple UAVs in MUEAC can fully utilize UAV resources and achieve load balancing. Furthermore, for divisible tasks with data-dependent characteristics, using partial offloading makes task scheduling more flexible compared to binary offloading, thus reducing task processing delay. Therefore, we propose a software defined networking enhanced cooperative MUEAC system. To minimize the processing delay of divisible tasks, we study the problem of joint task scheduling and computing resource allocation under task data dependency and UAV energy consumption constraints. To solve the non-convex problem, a multi-UAV cooperative communication and computing optimization (MCCCO) scheme is proposed. Experimental results corroborate that MCCCO can achieve better performance in task processing delay reduction and load balancing on UAV energy consumption than the traditional schemes.

Klaus I. Pedersen, Roberto Maldonado, Guillermo Pocovi, Enric Juan, Mads Lauridsen, I. Kovács, Mads Brix, Jeroen Wigard

In this tutorial we present recipes for dynamic system-level simulations (SLSs) of 5G and beyond cellular radio systems. A key ingredient for such SLSs is selection of proper models to make sure that the performance determining effects are properly reflected to ensure output of realistic radio performance results. We therefore present a significant number of SLS models and related methodologies for a variety of use cases. Our focus is on generally accepted models that are largely supported by academia and industrial players and adopted by 3GPP as being realistic. Among others, we touch on deployment models, traffic models, non-terrestrial cellular networks with satellites, SLS methodologies for Machine Learning (ML) enabled air-interface solutions, and many more. We also present several recommendations for best practices related to preparing and running detailed SLS campaigns, and agile software engineering considerations. Throughout the article we use the 3GPP defined 5G and 5G-Advanced systems to illustrate our points, extending it also into the 6G-era that is predicted to build on alike SLS methodologies and best practices.

Claudia Carballo González, E. F. Pupo, Eneko Iradier, P. Angueira, Maurizio Murroni, J. Montalbán

5G-Advanced and Beyond claims a 3D ecosystem with cooperation between terrestrial and non-terrestrial networks to achieve seamless coverage, improve capacity, and enable advanced applications with strict quality of service (QoS) requirements. This complex environment requires a disaggregated Radio Access Network (RAN) deployment with open interfaces, such as the architecture promoted by the O-RAN Alliance. This architecture, supporting the slicing paradigm, is a prominent solution to guarantee dynamism and differentiated traffic management. Furthermore, intelligence is critical for future wireless networks to enable Machine Learning (ML)-based optimization for autonomous RANs, handling ultra-dense heterogeneous environments, and adapting to numerous scenarios. This paper presents an enhanced Dynamic Radio Access Network Selection (eDRANS) algorithm based on Federated Double Deep Q-Network (F-DDQN) and inserted in the novel O-RAN architecture. The proposal selects the most suitable base station (BS) to satisfy multiple service requests, optimizing QoS and slicing resource utilization. Moreover, the solution employs a Cooperative Game Theory (CGT) approach to manage resources in overload situations. This load-balancing process enables the acceptance of new clients without abruptly degrading the active users' perception. eDRANS is adapted to diverse network conditions, multiple service constraints, and several user types with different priorities and mobility behaviors. The proposal is validated through network-level simulations, recreating a heterogeneous environment composed of terrestrial-airborne nodes and using the Max-SINR criterion, a heuristic algorithm, and centralized and distributed ML solutions as benchmarks. Results show that eDRANS correctly learns during multiple trial-and-error interactions with the environment, fulfilling the Service Level Agreement (SLA) and maximizing user satisfaction.

Xingqin Lin

As the telecommunications industry stands at the crossroads between the fifth generation (5G) and sixth generation (6G) of mobile communications, the 3rd generation partnership project (3GPP) Release 20 emerges as a pivotal point of transition. By striking a balance between enhancing 5G-Advanced capabilities and setting the stage for 6G, Release 20 provides the crucial foundation upon which future mobile communication standards and deployments will be built. This article examines these dual objectives, outlining the key enhancements, the motivations behind them, and their implications for the future of mobile communications.