A deep reinforcement learning for energy efficient resource allocation Intelligent Reflecting Surface (IRS) driven Non-Orthogonal Multiple Access Beamforming (NOMA-BF)

Abstract

This paper proposes a deep reinforcement learning centralized intelligent reflective surface (IRS) assisted non-orthogonal multiple access beamforming (NOMA-BF) system to improve the capacity gains and energy efficiency of next generation wireless communication networks. A single IRS is deployed at base station (BS) and consists of with R reflecting elements which is used to communicate with N single receiver antennas, referred to as centralized deployment of IRS- assisted NOMA. In the proposed central control scheme, we consider the practical situation where the instantaneous Channel State Information (CSI) of the channel between the IRS and the users is unknown. However, its long-term average is known and therefore makes the system more practical. Consequently, we exploit deep reinforcement technique learning technique to investigate the phase shift design and to tackle optimization problem. The energy efficiency problem is formulated as a non-convex optimization problem and is solved using deep and reinforcement learning algorithms. The first order Taylor approximation and difference of convex (DC) programming method using MATLAB tool box has been adopted to obtain the optimal values for power allocation coefficients. For interference mitigation, efficient user clustering along with beamforming algorithm is exploited. The NOMA user interference is tackled efficiently through the proposed algorithms that compute power allocation coefficients along with desirable phase shifts. The closed form expressions for achievable rates and SINR are derived under the required constraints. Simulation results demonstrate that proposed deep learning and reinforcement learning algorithms achieve improved performances in terms of sum rate, energy efficiency and coverage with lower computational complexity and symbol error rate. The computational complexity reduction has been proved both analytically and graphically and is in agreement.