Fractional function energy efficiency optimization in wireless networks: A graph convolutional network approach

Jian Zhang, Chunwei Miao, Jiaqi Huang, Rongchang Zhao

Abstract: Energy efficiency (EE) in large-scale high-density ad hoc networks is an NP-hard problem of fractional function optimization. Most traditional algorithms in the literature focus only on power control, mainly optimizing the solution of power control with the objective of sum-rate maximization or weighted sum-rate maximization, etc., and thus are unsuitable for solving fractional function EE optimization problems. In this paper, the architectural design of the neural network algorithm is directly guided with the objective of maximizing EE. Specifically, we first prove that the EE problem can be described as a graph optimization problem with permutation equivariance. Then, we propose an energy-efficient optimized graph convolutional neural network (EEOGCN) algorithm that satisfies permutation equivariance. Experiments show that the algorithm has better energy efficiency performance compared to other algorithms, can be directly applied to weighted energy efficiency optimization and beamforming energy efficiency optimization problems, and exhibits good generalization performance and scalability to large-scale high-density problems. In particular, the proposed method is computationally efficient and can solve the weighted energy efficiency optimization problem for 3000 transceiver pairs on a single GPU within 2 ms.