ScholarWorks@중앙대학교

초록

Rate-splitting multiple access (RSMA) and successive interference cancellation (SIC) are essential approaches in the next-generation communication systems that boost spectrum efficiency by effectively managing and mitigating interference between multiple signals. However, a challenge arises in ensuring that users can distinguish the common message from the remaining non-decoded private messages without considering a separate SIC constraint per user. This imperfection cancellation leads to residual interference from the common stream that remains in the received signal. This work investigates the maximization of the weighted sum-rate (WSR) in single-layer RSMA multiple input single output (MISO) downlink network by proposing explicit SIC constraints. In particular, we suggest an approach that initially addresses the critical problem of allocating power and precoding vectors for streams using a deep reinforcement learning (DRL) method, and then determines the user-specific allocations within the common rate to meet the criteria of users' minimum rate by solving a linear programming problem. Simulation results exhibit the supremacy of the proposed DRL framework over SDMA and other DRL approaches in terms of spectral efficiency leading to an improvement of approximately 30% of WSR in several scenarios.

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