Multi-Agent Reinforcement Learning Counteracts Delayed CSI in Multi-Satellite Systems
The integration of satellite communication networks with next-generation (NG) technologies is a promising approach towards global connectivity. However, the quality of services is highly dependant on the availability of accurate channel state information (CSI). Channel estimation in satellite communications is challenging due to the high propagation delay between terrestrial users and satellites, which results in outdated CSI observations on the satellite side. In this paper, we study the downlink transmission of multiple satellites acting as distributed base stations (BS) to mobile terrestrial users. We propose a multi-agent reinforcement learning (MARL) algorithm which aims for maximisi...
| Authors | Marios Aristodemou, Yasaman Omid, Sangarapillai Lambotharan et al. |
|---|---|
| Primary category | cs.IT |
| Published | 17 March 2026 |
| Last updated at source | 5 October 2026 |
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