Multi-Agent DRL Approach for Energy-Efficient Resource Allocation in URLLC-Enabled Grant-Free NOMA Systems

Abstract

Grant-free non-orthogonal multiple access (GF-NOMA) has emerged as a promising access technology for the fifth generation and beyond wireless networks that enable ultra-reliable and low-latency communications (URLLC) to ensure low access latency and high connectivity density. Furthermore, designing energy-efficient (EE) resource allocation strategies is a crucial aspect of future cellular system development. Taking these goals into account, this paper proposes an EE sub-channel and power allocation strategy for URLLC-enabled GF-NOMA (URLLC-GF-NOMA) systems based on multi-agent (MA) deep reinforcement learning (MADRL). In particular, the URLLC-GF-NOMA methods using MA dueling double deep Q network (MA3DQN), MA double deep Q network (MA2DQN), and MA deep Q network (MADQN) techniques are designed to enable users to select the most appropriate sub-channel and transmission power for their communications. The aim is to build an efficient MADRL-based solution, ensuring rapid convergence with small signaling overhead, to maximize the network EE while fulfilling the URLLC requirements of all users. Simulation results show that the MADQN and MA2DQN methods, which have lower complexity than MA3DQN, are more appropriate for the URLLC-GF-NOMA systems under consideration. Moreover, our proposed methods exhibit superior convergence characteristics, a reduction in signaling overhead, and enhanced EE performance compared to other benchmark strategies.

Keywords

• Energy efficiency
• grant-free NOMA
• multi-agent deep reinforcement learning
• URLLC