Flex-Net: A Graph Neural Network Approach to Resource Management in Flexible Duplex Networks
This addresses resource management challenges in wireless networks, offering a scalable solution for large networks, though it is incremental as it applies GNNs to a specific domain problem.
The paper tackled the NP-hard sum-rate maximization problem in flexible duplex networks by proposing Flex-Net, a Graph Neural Network architecture that jointly optimizes communication direction and transmission power, achieving near-optimal performance with low computational complexity.
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.