We consider the fairness in cooperative data exchange (CDE) problem among a set of wireless clients. In this system, each client initially obtains a subset of the packets. They exchange packets in order to reconstruct the entire packet set. We study the problem of how to find a transmission strategy that distributes the communication load most evenly in all strategies that have the same sum-rate (the total number of transmissions) and achieve universal recovery (the situation when all clients recover the packet set). We formulate this problem by a discrete minimization problem and prove its $M$-convexity. We show that our results can also be proved by the submodularity of the feasible region shown in previous works and are closely related to the resource allocation problems under submodular constraints. To solve this problem, we propose to use a steepest descent algorithm (SDA) based on $M$-convexity. By varying the number of clients and packets, we compare SDA with a deterministic algorithm (DA) based on submodularity in terms of convergence performance and complexity. The results show that for the problem of finding the fairest and minimum sum-rate strategy for the CDE problem SDA is more efficient than DA when the number of clients is up to five.
Energy-Efficient Processing and Robust Wireless Cooperative Transmission for Edge Inference
ShanghaiTech University（上海科技大学）；Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences（中国科学院上海微系统与信息技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；University of Toronto（多伦多大学）；University of California at Davis（加州大学戴维斯分校）
Distributed Reinforcement Learning for Age of Information Minimization in Real-Time IoT Systems
Beijing University of Posts and Telecommunications（北京邮电大学）；Princeton University（普林斯顿大学）；Virginia Tech（弗吉尼亚理工大学）；Shenzhen Research Institute of Big Data (SRIBD)（深圳市大数据研究院）；Future Network of Intelligence Institute (FNii), Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）未来智联网络研究院）
