Sum-Rate Maximization of RSMA-based Aerial Communications with Energy
  Harvesting: A Reinforcement Learning Approach
Jaehyup Seong Mesut Toka Wonjae Shin
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.