Outage-Constrained Robust Resource Allocation Framework for IRS-Empowered NOMA Systems: A DRL-Based Joint Design

Abstract

In this paper, we propose a robust resource allocation framework for an intelligent reflecting surface (IRS)-assisted multiple-input single-output (MISO) non-orthogonal multiple access (NOMA) system. In particular, a long-term robust sum-rate maximization problem is considered. The impacts of imperfect channel estimation on both the transmitter and the receiver are taken into account with an outage-constrained robust design approach. More specifically, the statistical error model is used to model the unbounded channel uncertainty in the system. However, the joint robust resource allocation problem is a mixed-integer optimization problem, which cannot be solved directly using conventional optimization algorithms. A correlation-based user pairing algorithm is proposed to group the users into clusters. Furthermore, the resource allocation problem with clustered users is reformulated as a reinforcement learning environment. Subsequently, a twin-delayed deep deterministic policy gradient (TD3) agent is developed to solve the outage-constrained robust resource allocation problem. Extensive simulation results are provided to demonstrate the superior performance of the developed TD3 agent over existing algorithms in the literature.

Keywords

• IRS-assisted MISO-NOMA systems
• robust resource allocation techniques
• deep reinforcement learning
• imperfect CSI