面向安全鲁棒的无细胞大规模多输入多输出（CF-mMIMO）系统：一种多阶段框架
Toward Security-Resilient Cell-Free Massive MIMO: A Multi-Stage Framework
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中文总结 AI 辅助
本文针对受主动导频欺骗攻击的CF-mMIMO系统，提出含吸收、恢复阶段的多阶段安全传输框架，结合PPZF策略与AN，经SCA求解非凸问题，性能优于基准方案。
中文摘要 AI 辅助
本文针对遭受主动导频欺骗攻击的无细胞大规模多输入多输出（CF-mMIMO）系统，提出了一种鲁棒的安全弹性传输框架。作为基准，首先在无攻击条件下表征系统性能，以建立目标用户攻击前的服务水平。检测到攻击后，系统进入吸收阶段，在此阶段中，利用受污染的信道状态信息（CSI）在有限的接入点（AP）子集上自适应调整功率分配，该阶段可快速补偿性能下降，同时保持较低的操作开销。一旦保密频谱效率（SSE）恢复到规定的损失水平，所得的功率分配将初始化恢复阶段，在此阶段中，对所有AP的发射功率进行联合优化，并且保护性部分迫零（PPZF）策略进一步提升保密性；与此同时，在考虑大尺度衰落不确定性的最坏情况窃听场景下，引入人工噪声（AN）。由此产生的依赖阶段的非凸问题被纳入统一框架，并使用连续凸近似（SCA）求解。数值结果表明，所提方案实现了有效的时间-质量权衡，同时保持了最高的恢复保密性；在代表性设置中，其相对于各基准方案分别实现了高达3.6%、15.7%和56%的增益，在窃听者信道不确定性下也有类似的提升。
英文摘要
This paper develops a robust security-resilient transmission framework for cell-free massive multiple-input multiple-output (CF-mMIMO) systems under active pilot spoofing attacks. As a baseline, system performance is characterized under attack-free conditions to establish the target user's pre-attack service level. Upon attack detection, the system enters an absorption phase, during which, power allocation is adaptively adjusted across a limited subset of access points (APs) using contaminated channel state information (CSI). This phase quickly compensates for performance degradation while maintaining low operational overhead. Once the secrecy spectral efficiency (SSE) recovers to a prescribed loss level, the resulting power allocation initializes the restoration phase. Here, the transmit powers of all APs are jointly optimized, and a protective partial zero-forcing (PPZF) strategy further improves secrecy. In parallel, artificial noise (AN) is incorporated under a worst-case eavesdropping scenario accounting for large-scale fading uncertainty. The resulting stage-dependent non-convex problems are formulated within a unified framework and solved using successive convex approximation (SCA). Numerical results demonstrate that the proposed scheme achieves an effective time-quality tradeoff while maintaining the highest recovered secrecy; in a representative setup, it achieves gains of up to 3.6%, 15.7%, and 56% over the respective baseline schemes, with similar improvements under eavesdropper's channel uncertainty.
发表机构
- Queen’s University Belfast（贝尔法斯特女王大学）
机构由 AI 辅助整理，请以论文原文为准。
