基于知识蒸馏的鲁棒分布式多卫星大规模MIMO传输
Robust Decentralized Multi-Satellite Massive MIMO Transmission via Knowledge Distillation
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中文总结 AI 辅助
本文针对不完善sCSI下的多卫星大规模MIMO系统，提出结合角度相位误差校准的混合KD框架，将集中式教师NN的协作预编码知识蒸馏至分布式学生NN，提升了和速率并保持鲁棒性。
中文摘要 AI 辅助
本文研究了在不完善的统计信道状态信息（sCSI）下，协作多卫星大规模多输入多输出（MIMO）系统的鲁棒分布式传输。在考虑的场景中，每颗卫星可完全获取自身本地信息，但由于星间链路（ISL）受限，仅能从其他卫星接收部分信息，且仅能获得不完善的sCSI。为应对这些挑战，本文提出一种知识蒸馏（KD）框架，将协作预编码知识从集中式教师神经网络（NN）迁移至轻量级分布式学生NN。具体而言，全局清洁教师在离线训练期间聚合所有卫星的信息并访问准确的sCSI，将协作预编码知识迁移至部分噪声学生，学生依赖完整本地信息、与其他卫星交换的有限信息以及含误差的sCSI进行本地预编码。教师NN结合分块自注意力与双轴注意力，学习用户间干扰和卫星间协作，而每个学生NN采用紧凑的单卫星架构以实现高效星上推理。教师从全局清洁输入中学习高质量的加权最小均方误差预编码策略，随后将其蒸馏至基于部分噪声输入运行的学生。为缓解由此产生的教师-学生性能差距，本文开发了一种混合KD机制，具有明确的角度和相位误差校准。仿真结果表明，所提框架显著提升了分布式和速率性能，并在多种配置下保持鲁棒性。
英文摘要
This paper investigates robust decentralized transmission for cooperative multi-satellite massive multiple-input multiple-output (MIMO) systems under imperfect statistical channel state information (sCSI). In the considered scenario, each satellite has complete access to its local information but receives partial information from other satellites due to limited inter-satellite links (ISLs), with only imperfect sCSI available. To address these challenges, we propose a knowledge distillation (KD) framework that transfers cooperative precoding knowledge from a centralized teacher neural network (NN) to lightweight decentralized student NNs. Specifically, a global-clean teacher, aggregating information from all satellites and accessing accurate sCSI during offline training, transfers its cooperative precoding knowledge to partial-noisy students, relying on complete local information, limited information exchanged by other satellites, and error-corrupted sCSI for local precoding. The teacher NN combines patch-wise self-attention with dual-axis attention to learn inter-user interference and inter-satellite coordination, whereas each student NN adopts a compact per-satellite architecture for efficient onboard inference. The teacher learns a high-quality weighted minimum mean square error precoding policy from global-clean inputs, which is then distilled into the students operating on partial-noisy inputs. To mitigate the resulting teacher-student performance gap, we develop a hybrid KD mechanism with explicit angle- and phase-error calibration. Simulation results demonstrate that the proposed framework significantly enhances the decentralized sum-rate performance and remains robust under diverse configurations.
发表机构
- National Mobile Communications Research Laboratory, Southeast University（东南大学国家移动通信重点实验室）
- Purple Mountain Laboratories（紫金山实验室）
- University of Luxembourg（卢森堡大学）
- Peng Cheng Laboratory（鹏城实验室）
- China Satellite Network Group Company Ltd.（中国卫星网络集团有限公司）
机构由 AI 辅助整理，请以论文原文为准。
