The SOLIDS 6G Mobile Network Architecture:Driving Forces,Features,and Functional Topology

With the large-scale commercial launch of fifth generation(5G)mobile network,the development of new services and applications catering to the year 2030,along with the deep convergence of information,communication,and data technologies(ICDT),and the lessons and experiences from 5G practice will drive the evolution of the next generation of mobile networks.This article surveys the history and driving forces of the evolution of the mobile network architecture and proposes a logical function architecture for sixth generation(6G)mobile network.The proposed 6G network architecture is termed SOLIDS(related to the following basic features:soft,on-demand fulfillment,lite,native intelligence,digital twin,and native security),which can support self-generation,self-healing,self-evolution,and self-immunity without human involvement and address the primary issues in the legacy 5G network(e.g.,high cost,high power consumption,and highly complicated operation and maintenance),significantly well.

Time Efficient Joint Optimization Federated Learning over Wireless Communication Networks

Artificial intelligence(AI)has made a profound impact on our daily life.The 6 th generation mobile networks(6G)should be designed to enable AI services.The native intelligence is introduced as an important feature in 6G.6G native AI network is realized by the philosophy of federated learning(FL)to ensure data security and privacy.Federated learning over wireless communication networks is treated as a potential solution to realize native AI.However,introducing FL in the 6G will lead to expansive communication cost and unstable FL convergence with unreliable air interface.In this paper,we propose a solution for FL over wireless networks and analyze the training efficiency.To make full use of the advantages of the proposed network,we introduce a communication-FL joint optimization(CFJO)algorithm by jointly considering the effects of uplink resource,energy consumption and latency constraints.CFJO derives a transmission strategy with resource allocation and retransmissions to reduce the wireless transmission interruption probability and model upload latency.The simulation results show that CFJO significantly improves the model training efficiency and convergence performance with lower interruption probability under the latency constraint.

Micro-Service-Based Radio Access Network

The convergence of information,communication,and data technologies(ICDT)has been identified as one of the developing trends of the sixth generation(6G)network.Service-based architecture(SBA)as one of the promising information technology,has been preliminarily introduced into the fifth generation(5G)core network(CN)and successfully commercialized,which verifies its feasibility and effectiveness.However,SBA mainly focuses on the control plane of CN at present and the SBA-CN user plane is being studied by the industry.In addition to further evolving the SBA-CN,SBA radio access network(RAN)should also be considered to enable an end-toend SBA,so as to meet more comprehensive and extreme requirements of future applications,as well as support fast rollout requirements of RAN devices.

Dynamic user-centric multi-dimensional resource allocation for a wide-area coverage signaling cell based on DQN

The rapid development of communications industry has spawned more new services and applications.The sixth-generation wireless communication system(6G)network is faced with more stringent and diverse requirements.While ensuring performance requirements,such as high data rate and low latency,the problem of high energy consumption in the fifth-generation wireless communication system(5G)network has also become one of the problems to be solved in 6G.The wide-area coverage signaling cell technology conforms to the future development trend of radio access networks,and has the advantages of reducing network energy consumption and improving resource utilization.In wide-area coverage signaling cells,on-demand multi-dimensional resource allocation is an important technical means to ensure the ultimate performance requirements of users,and its effect will affect the efficiency of network resource utilization.This paper constructs a user-centric dynamic allocation model of wireless resources,and proposes a deep Q-network based dynamic resource allocation algorithm.The algorithm can realize dynamic and flexible admission control and multi-dimensional resource allocation in wide-area coverage signaling cells according to the data rate and latency demands of users.According to the simulation results,the proposed algorithm can effectively improve the average user experience on a long time scale,and ensure network users a high data rate and low energy consumption.