Deep Reinforcement Learning Enhanced Rate-Splitting Multiple Access for Interference Mitigation
This study explores the application of the rate-splitting multiple access (RSMA) technique,
vital for interference mitigation in modern communication systems. It investigates the use of
precoding methods in RSMA, especially in complex multiple-antenna interference channels,
employing deep reinforcement learning. The aim is to optimize precoders and power
allocation for common and private data streams involving multiple decision-makers. A multi-
agent deep deterministic policy gradient (MADDPG) framework is employed to address this …
vital for interference mitigation in modern communication systems. It investigates the use of
precoding methods in RSMA, especially in complex multiple-antenna interference channels,
employing deep reinforcement learning. The aim is to optimize precoders and power
allocation for common and private data streams involving multiple decision-makers. A multi-
agent deep deterministic policy gradient (MADDPG) framework is employed to address this …
Deep Reinforcement Learning Enhanced Rate-Splitting Multiple Access for Interference Mitigation
O Nuri Irkicatal, E Tugce Ceran, M Yuksel - arXiv e-prints, 2024 - ui.adsabs.harvard.edu
This study explores the application of the rate-splitting multiple access (RSMA) technique,
vital for interference mitigation in modern communication systems. It investigates the use of
precoding methods in RSMA, especially in complex multiple-antenna interference channels,
employing deep reinforcement learning. The aim is to optimize precoders and power
allocation for common and private data streams involving multiple decision-makers. A multi-
agent deep deterministic policy gradient (MADDPG) framework is employed to address this …
vital for interference mitigation in modern communication systems. It investigates the use of
precoding methods in RSMA, especially in complex multiple-antenna interference channels,
employing deep reinforcement learning. The aim is to optimize precoders and power
allocation for common and private data streams involving multiple decision-makers. A multi-
agent deep deterministic policy gradient (MADDPG) framework is employed to address this …
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