Flexible Robust Beamforming for Multibeam Satellite Downlink using Reinforcement Learning
ICC 2024 - IEEE International Conference on Communications(2024)
摘要
Low Earth Orbit (LEO) satellite-to-handheld connections herald a new era in
satellite communications. Space-Division Multiple Access (SDMA) precoding is a
method that mitigates interference among satellite beams, boosting spectral
efficiency. While optimal SDMA precoding solutions have been proposed for ideal
channel knowledge in various scenarios, addressing robust precoding with
imperfect channel information has primarily been limited to simplified models.
However, these models might not capture the complexity of LEO satellite
applications. We use the Soft Actor-Critic (SAC) deep Reinforcement Learning
(RL) method to learn robust precoding strategies without the need for explicit
insights into the system conditions and imperfections. Our results show
flexibility to adapt to arbitrary system configurations while performing
strongly in terms of achievable rate and robustness to disruptive influences
compared to analytical benchmark precoders.
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关键词
6G,Multi-user beamforming,3D networks,Low Earth Orbit (LEO),Satellite Communications,Machine Learning (ML),deep Reinforcement Learning (RL)
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