Intelligence Driven Wireless Networks in B5G and 6G Era:A Survey

As the wireless communication network undergoes continuous expansion,the challenges associated with network management and optimization are becoming increasingly complex.To address these challenges,the emerging artificial intelligence(AI)and machine learning(ML)technologies have been introduced as a powerful solution.They empower wireless networks to operate autonomously,predictively,ondemand,and with smart functionality,offering a promising resolution to intricate optimization problems.This paper aims to delve into the prevalent applications of AI/ML technologies in the optimization of wireless networks.The paper not only provides insights into the current landscape but also outlines our vision for the future and considerations regarding the development of an intelligent 6G network.

Green Concerns in Federated Learning over 6G

As Information,Communications,and Data Technology(ICDT)are deeply integrated,the research of 6G gradually rises.Meanwhile,federated learning(FL)as a distributed artificial intelligence(AI)framework is generally believed to be the most promising solution to achieve“Native AI”in 6G.While the adoption of energy as a metric in AI and wireless networks is emerging,most studies still focused on obtaining high levels of accuracy,with little consideration on new features of future networks and their possible impact on energy consumption.To address this issue,this article focuses on green concerns in FL over 6G.We first analyze and summarize major energy consumption challenges caused by technical characteristics of FL and the dynamical heterogeneity of 6G networks,and model the energy consumption in FL over 6G from aspects of computation and communication.We classify and summarize the basic ways to reduce energy,and present several feasible green designs for FL-based 6G network architecture from three perspectives.According to the simulation results,we provide a useful guideline to researchers that different schemes should be used to achieve the minimum energy consumption at a reasonable cost of learning accuracy for different network scenarios and service requirements in FL-based 6G network.

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.