Optimal Power Allocation for Rate Splitting Communications with Deep Reinforcement Learning
This work addresses energy and spectral efficiency problems in wireless communications, but it is incremental as it applies an existing method to a specific domain.
The paper tackles the challenge of optimizing power allocation in Rate Splitting Multiple Access (RSMA) networks under uncertain channel conditions by developing a deep reinforcement learning framework, achieving improved average sum-rate compared to baseline schemes.
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.