该研究深入探讨了天地一体化网络 (SAGIN) 中低地球轨道 (LEO) 卫星和无人机 (UAV) 共存导致的干扰管理挑战。为了解决传统方法的局限性，作者提出了一种名为 全分布式速率分裂多址 (FD-RSMA) 的新型方案，该方案利用深度学习 (DL) 辅助的 GruCN 模型来优化预编码器设计和速率分配。研究结果表明，与现有技术相比，FD-RSMA 能够显著提升加权遍历和速率 (WESR)，并且 GruCN 模型能够大幅缩短处理时间，从而提高了 SAGIN 在实际部署中的效率和可行性。

Interference Management in Space-Air-Ground Integrated Networks With Fully Distributed Rate-Splitting Multiple Access

Abstract:

Despite the allure of ubiquitous, high-speed, and low-latency connectivity offered by Space-Air-Ground Integrated Networks (SAGINs), the co-existence of Low Earth Orbit (LEO) satellites and Unmanned Aerial Vehicles (UAVs) within the same frequency band poses significant challenges in interference management. Traditional optimization approaches, requiring seconds or even minutes for beamforming design, simply cannot keep pace with this dynamic environment. This work addresses these challenges by proposing a Fully-Distributed Rate-Splitting Multiple Access (FD-RSMA), which enables efficient cross-system interference management in SAGINs with statistical Channel State Information (CSI) at the Transmitter (CSIT). Building upon FD-RSMA, we study the precoder design of LEO satellites and UAVs along with common rate allocations of RSMA to maximize Weighted Ergodic Sum Rate (WESR). To handle channel randomness, we employ a Sample Average Approximation (SAA) approach. Furthermore, a Deep Learning (DL)-based precoder design algorithm, called GruCN, which marries the advantages of Gate Recurrent Unit (GRU) and Convolutional Neural Network (CNN), is proposed to efficiently tackle the non-convex optimization problem. Numerical results demonstrate the effectiveness and efficiency of our proposed DL-assisted FD-RSMA. Compared to conventional RSMA approaches, FD-RSMA improves up to 20% of WESR performance, while the GruCN achieves around 50% higher WESR performance and up to four orders of magnitude lower processing time than the conventional optimization approaches.

尽管天地一体化网络 (SAGIN) 提供无处不在的高速低延迟连接，但低地球轨道 (LEO) 卫星和无人机 (UAV) 在同一频段的共存对干扰管理提出了重大挑战。传统的优化方法需要几秒甚至几分钟的时间进行波束赋形设计，根本无法跟上这种动态环境的步伐。本文通过提出一种全分布式速率分割多址接入 (FD-RSMA) 来应对这些挑战，该方案利用发射端统计信道状态信息 (CSIT)，在 SAGIN 中实现高效的跨系统干扰管理。基于 FD-RSMA，我们研究了 LEO 卫星和无人机的预编码器设计以及 RSMA 的通用速率分配，以最大化加权遍历和速率 (WESR)。为了处理信道随机性，我们采用了样本平均近似 (SAA) 方法。此外，我们提出了一种基于深度学习 (DL) 的预编码器设计算法 GruCN，该算法结合了门循环单元 (GRU) 和卷积神经网络 (CNN) 的优势，能够有效解决非凸优化问题。数值结果证明了我们提出的深度学习辅助 FD-RSMA 的有效性和高效性。与传统的 RSMA 方法相比，FD-RSMA 将 WESR 性能提升了 20%，而 GruCN 将 WESR 性能提升了约 50%，并且处理时间比传统优化方法降低了四个数量级。

Published in: IEEE Transactions on Wireless Communications ( Volume: 24, Issue: 1, January 2025)

Page(s): 149 - 164

Date of Publication: 07 November 2024

ISSN Information:

Print ISSN: 1536-1276

Electronic ISSN: 1558-2248