A Deep Reinforcement Learning Approach to IRS-assisted MU-MIMO
Communication Systems
Abstract
The deep reinforcement learning (DRL)-based deep deterministic policy
gradient (DDPG) framework is proposed to solve the joint optimization of
the IRS phase-shift matrix and the precoding matrix in an IRS-assisted
multi-stream multi-user MIMO communication.
The combination of multiple-input multiple-output(MIMO) communications
and intelligent reflecting surfaces(IRSs) is foreseen as a key enabler
of beyond 5G (B5G) and 6Gsystems. In this work, we develop an innovative
deep reinforcement learning (DRL)-based approach to the joint
optimization of the MIMO precoders and the IRS phase-shift matrices that
is proved to be efficient in high dimensional systems. The proposed
approach is termed deep deterministic policy gradient (DDPG)and
maximizes the sum rate of an IRS-assisted multi-stream(MS) multi-user
MIMO (MU-MIMO) system by learning the best matrix configuration through
online trial-and-error interactions. The proposed approach is formulated
in terms of continuous state and action spaces, and a sum-rate-based
reward function. The computational complexity is reduced by using
artificial neural networks (ANNs) for function approximations and it is
shown that the proposed solution scales better than other
state-of-the-art methods, while reaching a competitive performance.