Beamforming and Resource Allocation for Delay Minimization in RIS-Assisted OFDM Systems

TL;DR

This paper presents a hybrid deep reinforcement learning approach for joint beamforming and resource allocation in RIS-assisted OFDM systems, effectively minimizing average delay and improving system robustness and fairness.

Contribution

It introduces a novel hybrid DRL framework with multi-agent strategies and transfer learning for efficient delay minimization in RIS-OFDM systems.

Findings

Significant delay reduction compared to baseline methods

Enhanced resource allocation efficiency and system robustness

Faster convergence through transfer learning

Abstract

This paper investigates a joint beamforming and resource allocation problem in downlink reconfigurable intelligent surface (RIS)-assisted orthogonal frequency division multiplexing (OFDM) systems to minimize the average delay, where data packets for each user arrive at the base station (BS) stochastically. The sequential optimization problem is inherently a Markov decision process (MDP), thus falling within the remit of reinforcement learning. To effectively handle the mixed action space and reduce the state space dimensionality, a hybrid deep reinforcement learning (DRL) approach is proposed. Specifically, proximal policy optimization (PPO)-Theta is employed to optimize the RIS phase shift design, while PPO-N is responsible for subcarrier allocation decisions. The active beamforming at the BS is then derived from the jointly optimized RIS phase shifts and subcarrier allocation decisions. To further mitigate the curse of dimensionality associated with subcarrier allocation, a multi-agent strategy is introduced to optimize the subcarrier allocation indicators more efficiently. Moreover, to achieve more adaptive resource allocation and accurately capture the network dynamics, key factors closely related to average delay, such as the number of backlogged packets in buffers and current packet arrivals, are incorporated into the state space. Furthermore, a transfer learning framework is introduced to enhance the training efficiency and accelerate convergence. Simulation results demonstrate that the proposed algorithm significantly reduces the average delay, enhances resource allocation efficiency, and achieves superior system robustness and fairness compared to baseline methods.