This study explores the application of RSMA with deep reinforcement learning to mitigate interference in modern communication systems. The proposed method shows promising results compared to benchmark schemes like MRT, ZF, and leakage-based precoding. The research highlights the significance of rate-splitting in enhancing communication efficiency.
The imperative for high data rates in 6G networks is driven by demands of data-intensive applications. Advanced techniques like RSMA effectively manage interference in ultra-dense deployments. Researchers explore applications, complexities, and practical implementations of rate-splitting.
While RSMA offers flexibility, tackling non-convex optimization challenges is particularly challenging. Deep reinforcement learning is pivotal for addressing these challenges in RSMA. MADDPG framework enables decentralized execution for interference management without constant coordination.
The study compares the performance of proposed MADDPG algorithm against existing baseline schemes and upper bounds. It showcases the superiority of MADDPG with rate splitting. Channel estimation errors and decoding order selection are incorporated to enhance robustness.
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by Osman Nuri I... om arxiv.org 03-12-2024
https://arxiv.org/pdf/2403.05974.pdfDiepere vragen