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arXiv 2602.06982eess.SPcs.AIcs.ITcs.LGmath.IT
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基于RIS的深度强化学习用于空间-空-地一体化网络中的干扰抑制

Deep Reinforcement Learning for Interference Suppression in RIS-Aided Space-Air-Ground Integrated Networks

Department of Artificial Intelligence, Indian Institute of Technology Hyderabad, Telangana, India(印度海得拉巴印度理工学院人工智能系);School of Informatics, Kami, Kochi University of Technology, Kochi, Japan(日本科技大学信息学院);Graduate School of Information Sciences, Tohoku University, Sendai, Miyagi, Japan(东北大学信息科学研究生院)

Pujitha Mamillapalli, Shikhar Verma, Tiago Koketsu Rodrigues, Abhinav Kumar

AI总结:

本文提出基于RIS的深度强化学习方法,用于优化HAPS波束成形以抑制干扰,提升空间-空-地一体化网络的频谱效率。

摘要:

未来6G网络通过空间-空-地一体化网络(SAGINs)实现无处不在的连接,其中高空平台站(HAPSs)和卫星与地面系统互补,以提供大范围、低延迟的覆盖。然而,地面设备的快速增长加剧了地面与非地面段之间的频谱共享,导致严重的跨层干扰。特别是HAPS卫星上行与HAPS地面下行之间的频率共享提高了频谱效率,但受到HAPS天线后瓣引起的干扰影响。现有依赖于零力(ZF)码本的方法在高度动态信道条件下性能有限。为克服这一限制,我们采用基于RIS的HAPS-based SAGIN框架,并采用深度确定性策略梯度(DDPG)算法。所提出的DDPG框架优化HAPS波束成形权重,以形成指向干扰源的空间空洞,同时保持与所需信号的稳健链接。仿真结果表明,所提出的DDPG框架在不同RIS配置中均优于传统ZF波束成形,对于4×4 RIS配置,实现了高达11.3%的吞吐量提升,验证了其增强动态HAPS-based SAGINs频谱效率的适应能力。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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