用AI优化卫星通信波束，抑制干扰提升网络速度
Deep Reinforcement Learning for Interference Suppression in RIS-Aided Space-Air-Ground Integrated Networks
- 用深度强化学习动态调整波束方向，避开干扰源
- 在4×4智能表面配置下，速率最高提升11.3%
- 适合研究6G空天地一体化网络的工程师
未来6G网络通过空-天-地一体化网络（SAGIN）实现全域覆盖，高空气平台站（HAPS）与卫星补充地面系统，提供广域低延迟连接。然而，地面设备激增导致地面与非地面段间频谱共享加剧，造成严重跨层干扰。特别是HAPS卫星上行与地面下行共用频率时，因天线背瓣产生显著干扰。现有依赖零强制（ZF）码本的方法在高度动态信道下性能受限。为此，本文提出基于可重构智能表面（RIS）的HAPS-SAGIN框架，采用深度确定性策略梯度（DDPG）算法优化HAPS波束赋形权重，在抑制干扰源方向形成空间零点的同时，保持对目标信号的强连接。仿真结果表明，该框架在不同RIS配置下均优于传统ZF波束赋形，4×4 RIS配置下吞吐量最高提升11.3%，验证了其在动态HAPS-SAGIN中提升频谱效率的自适应能力。
原文摘要 · Abstract (English)
Future 6G networks envision ubiquitous connectivity through space-air-ground integrated networks (SAGINs), where high-altitude platform stations (HAPSs) and satellites complement terrestrial systems to provide wide-area, low-latency coverage. However, the rapid growth of terrestrial devices intensifies spectrum sharing between terrestrial and non-terrestrial segments, resulting in severe cross-tier interference. In particular, frequency sharing between the HAPS satellite uplink and HAPS ground downlink improves spectrum efficiency but suffers from interference caused by the HAPS antenna back-lobe. Existing approaches relying on zero-forcing (ZF) codebooks have limited performance under highly dynamic channel conditions. To overcome this limitation, we employ a reconfigurable intelligent surface (RIS)-aided HAPS-based SAGIN framework with a deep deterministic policy gradient (DDPG) algorithm. The proposed DDPG framework optimizes the HAPS beamforming weights to form spatial nulls toward interference sources while maintaining robust links to the desired signals. Simulation results demonstrate that the DDPG framework consistently outperforms conventional ZF beamforming among different RIS configurations, achieving up to \(11.3\%\) throughput improvement for a \(4\times4\) RIS configuration, validating its adaptive capability to enhance spectral efficiency in dynamic HAPS-based SAGINs.
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