# A Novel Deep Reinforcement Learning-based Approach for Enhancing Spectral Efficiency of IRS-assisted Wireless Systems

## TL;DR

This paper introduces a deep reinforcement learning approach to optimize spectral efficiency in IRS-assisted wireless systems, outperforming traditional optimization methods by jointly optimizing transmit beamforming and IRS phase shifts.

## Contribution

It presents a novel application of DDPG and TD3 algorithms for joint optimization in IRS-enhanced networks, addressing the non-convex problem more effectively.

## Key findings

- TD3 outperforms DDPG in various scenarios
- Neural network-based optimization improves spectral efficiency
- Deep RL methods outperform traditional optimization techniques

## Abstract

This letter investigates an intelligent reflecting surfaces (IRS)-enhanced network from spectral efficiency enhancement point of view for downlink multi-user (MU) multi-input-single-output systems (MISO). In contrast to previous works which mainly focused on alternative optimization methods, we investigate the non-convex joint optimization problem of the active transmit beamforming matrix at the base station together with the passive phase shift matrix at the IRS by utilizing two deep reinforcement learning frameworks, i. e., deep deterministic policy gradient (DDPG) and twin delayed DDPG (TD3). Simulation results reveal that the neural networks in the latter scheme perform generally more satisfactorily in various situations.