Neural Combinatorial Optimization for Throughput Maximization in IRS-Aided Systems

Intelligent reflecting surface (IRS) is a promising paradigm for enhancing the spectrum efficiency of wireless communication systems. In this paper, we study the joint uplink scheduling and phase shift control in IRS-aided systems. We formulate the throughput maximization problem as a combinatorial optimization problem. We decompose the problem into two subproblems for user scheduling and phase shift control, respectively. We propose a neural combinatorial optimization (NCO)-based algorithm, in which a near-optimal stochastic policy for user scheduling is learned by deep neural networks (DNNs) with attention mechanism, while the phase shifts of the IRS are optimized using fractional programming. Unlike alternating optimization-based approaches which obtain a suboptimal solution by iteratively solving two subproblems, the proposed NCO-based algorithm is capable of obtaining a near-optimal solution while each subproblem is required to be solved only once. Simulation results show that the proposed NCO-based algorithm achieves an aggregate throughput which is within 98% of the exhaustive search algorithm, and outperforms both greedy scheduling and random scheduling algorithms.