Sum-Rate Maximization of RSMA-based Aerial Communications with Energy Harvesting: A Reinforcement Learning Approach
This work addresses energy-efficient communication for aerial base stations serving multiple users, but it is incremental as it applies existing methods to a specific scenario.
The paper tackles the problem of maximizing long-term sum-rate for rate-splitting multiple access (RSMA)-based aerial communications with energy harvesting by jointly designing power and beamforming, using a deep reinforcement learning approach combined with sequential least squares programming. Numerical results demonstrate that the proposed scheme outperforms baseline methods in average sum-rate performance.
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.