This paper presents a hierarchical approach to resource allocation in open-access femtocell networks. The major challenge in femtocell networks is interference management which in our system, based on the Long Term Evolution (LTE) standard, translates to which user should be allocated which physical resource block (or fraction thereof) from which femtocell access point (FAP). The globally optimal solution requires integer programming and is mathematically intractable. We propose a hierarchical three-stage solution: first, the load of each FAP is estimated considering the number of users connected to the FAP, their average channel gain and required data rates. Second, based on each FAP's load, the physical resource blocks (PRBs) are allocated to FAPs in a manner that minimizes the interference by coloring the modified interference graph. Finally, the resource allocation is performed at each FAP considering users' instantaneous channel gain. The two major advantages of this suboptimal approach are the significantly reduced computation complexity and the fact that the proposed algorithm only uses information that is already likely to be available at the nodes executing the relevant optimization step. The performance of the proposed solution is evaluated in networks based on the LTE standard.
Rate Maximization for Multi-Waveguide PASS: A Hierarchical User Scheduling and Joint Optimization Framework
Beijing Jiaotong University（北京交通大学）；Lancaster University（兰卡斯特大学）；The University of Hong Kong（香港大学）；Queen Mary University of London（伦敦大学玛丽女王学院）；Kyung Hee University（庆熙大学）
Fair Resource Allocation For Hierarchical Federated Edge Learning in Space-Air-Ground Integrated Networks via Deep Reinforcement Learning with Hybrid Control
Hierarchical Learning for IRS-Assisted MEC Systems with Rate-Splitting Multiple Access
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Shenzhen Future Network of Intelligence Institute（深圳未来网络智能研究所）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen University of Advanced Technology（深圳高等研究院）；Shanghai Jiao Tong University（上海交通大学）；Nanyang Technological University（南洋理工大学）
With the emergence of more and more applications of Internet-of-Things (IoT) mobile devices (IMDs), a contradiction between mobile energy demand and limited battery capacity becomes increasingly prominent. In addition, in ultra-dense IoT networks, the ultra-densely deployed small base stations (SBSs) will consume a large amount of energy. To reduce the network-wide energy consumption and extend the standby time of IMDs and SBSs, under the proportional computation resource allocation and devices' latency constraints, we jointly perform the device association, computation offloading and resource…
