发表机构
Aalto University; Zhejiang University; Southern Illinois University; Villanova University（阿尔托大学; 浙江大学; 南伊利诺伊大学; 维拉诺瓦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对混合近场/远场无授权活动检测，提出协方差感知的MM-PGD检测器，联合更新活动向量，相比逐坐标基线漏检概率降低达20倍。
AI 中文摘要
无授权活动检测在混合近场（NF）和远场（FF）设备场景中是一个重要问题，可通过基于协方差的检测器解决。难点在于NF用户产生设备特定的结构化空间协方差，而FF用户可近似为各向同性协方差。在统一的莱斯模型下，我们首先将活动检测表述为全LM维向量化观测空间中的松弛最大似然问题。随后，我们开发了一种协方差感知的majorization-minimization投影梯度下降（MM-PGD）检测器。它联合更新完整活动向量，避免了NF区域中出现的逐坐标高秩子问题。在信噪比、天线数量和NF比例扫描的数值结果表明，MM-PGD的漏检概率比最强的逐坐标基线低至20倍。该优势在NF比例高时最为显著，而在全FF情况下，MM-PGD与最强的逐坐标基线性能相当。
英文摘要
Grant-free activity detection with mixed near-field (NF) and far-field (FF) devices is an important problem that can be addressed via covariance-based detectors. The difficulty is that NF users induce device-specific structured spatial covariances, whereas FF users are well approximated by isotropic covariances. Under a unified Rician model, we first formulate activity detection as a relaxed maximum-likelihood problem in the full LM-dimensional vectorized observation space. We then develop a covariance-aware majorization-minimization projected gradient descent (MM-PGD) detector. It updates the full activity vector jointly and avoids the per-coordinate high-rank subproblems that arise in the NF regime. Numerical results over SNR, antenna-count, and NF-ratio sweeps show that MM-PGD achieves up to 20x lower miss-detection probability than the strongest coordinate-wise baseline. The advantage is most pronounced at high NF ratios, while in the all-FF case MM-PGD performs on par with the strongest coordinate-wise baseline.
Comments5 pages, 3 figures. IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2026
