Sum-Rate Maximization of RSMA-based Aerial Communications with Energy Harvesting: A Reinforcement Learning Approach

TL;DR

This paper proposes a reinforcement learning-based method for optimizing power and beamforming in energy-harvesting RSMA aerial communications, enhancing long-term sum-rate performance while managing energy constraints.

Contribution

It introduces a novel joint power and beamforming design using DRL and SLSQP for RSMA-based aerial networks with energy harvesting, addressing long-term sum-rate maximization.

Findings

Proposed scheme outperforms baseline methods in average sum-rate.

DRL effectively manages power constraints in stochastic environments.

Sequential optimization improves precoding and power allocation.

Abstract

In this letter, we investigate a joint power and beamforming design problem for rate-splitting multiple access (RSMA)-based aerial communications with energy harvesting, where a self-sustainable aerial base station serves multiple users by utilizing the harvested energy. Considering maximizing the sum-rate from the long-term perspective, we utilize a deep reinforcement learning (DRL) approach, namely the soft actor-critic algorithm, to restrict the maximum transmission power at each time based on the stochastic property of the channel environment, harvested energy, and battery power information. Moreover, for designing precoders and power allocation among all the private/common streams of the RSMA, we employ sequential least squares programming (SLSQP) using the Han-Powell quasi-Newton method to maximize the sum-rate for the given transmission power via DRL. Numerical results show the superiority of the proposed scheme over several baseline methods in terms of the average sum-rate performance.