2023/01/20 by Tharaka Perera, Perera, Tharaka, Saman Atapattu +7 · 1 citation
Computer Science · Engineering · #Advanced MIMO Systems Optimization #Cooperative Communication and Network Coding #Energy Harvesting in Wireless Networks #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Networking and Internet Architecture (cs.NI) #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2301.11166
openalex publication_date 2023/01/20 · openalex created_date 2023/01/28 · openalex updated_date 2026/07/28
Flexible duplex networks allow users to dynamically employ uplink and downlink channels without static time scheduling, thereby utilizing the network resources efficiently. This work investigates the sum-rate maximization of flexible duplex networks. In particular, we consider a network with pairwise-fixed communication links. Corresponding combinatorial optimization is a non-deterministic polynomial (NP)-hard without a closed-form solution. In this respect, the existing heuristics entail high computational complexity, raising a scalability issue in large networks. Motivated by the recent success of Graph Neural Networks (GNNs) in solving NP-hard wireless resource management problems, we propose a novel GNN architecture, named Flex-Net, to jointly optimize the communication direction and transmission power. The proposed GNN produces near-optimal performance meanwhile maintaining a low computational complexity compared to the most commonly used techniques. Furthermore, our numerical results shed light on the advantages of using GNNs in terms of sample complexity, scalability, and generalization capability.