Optimal Power Allocation for Rate Splitting Communications with Deep Reinforcement Learning

TL;DR

This paper proposes a deep reinforcement learning framework to optimize power allocation in Rate Splitting Multiple Access networks, improving spectral efficiency without prior channel knowledge.

Contribution

It introduces a novel MDP-based model and a deep RL algorithm for dynamic power allocation in RSMA, handling channel uncertainty effectively.

Findings

Outperforms baseline schemes in sum-rate performance

Effectively manages interference in RSMA networks

Adapts to different power and QoS requirements

Abstract

This letter introduces a novel framework to optimize the power allocation for users in a Rate Splitting Multiple Access (RSMA) network. In the network, messages intended for users are split into different parts that are a single common part and respective private parts. This mechanism enables RSMA to flexibly manage interference and thus enhance energy and spectral efficiency. Although possessing outstanding advantages, optimizing power allocation in RSMA is very challenging under the uncertainty of the communication channel and the transmitter has limited knowledge of the channel information. To solve the problem, we first develop a Markov Decision Process framework to model the dynamic of the communication channel. The deep reinforcement algorithm is then proposed to find the optimal power allocation policy for the transmitter without requiring any prior information of the channel. The simulation results show that the proposed scheme can outperform baseline schemes in terms of average sum-rate under different power and QoS requirements.