混合近场与远场随机接入中活动检测的Majorization-Minimization框架
A Majorization-Minimization Framework for Activity Detection in Mixed Near-and-Far-Field Random Access
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中文总结 AI 辅助
针对混合近场与远场随机接入中的活动检测问题，提出统一协方差感知Rician框架及MM-PGD检测器，利用Kronecker-Woodbury实现可扩展似然评估，在近场用户比例增加时性能优势显著。
中文摘要 AI 辅助
大规模机器类通信中的免授权随机接入需要在配备M天线基站处从长度为L的上行导频中检测出小的活跃用户集合。经典的基于协方差的检测器主要建立在远场（FF）模型上，其中M个天线域观测被视为独立快照，推理简化为L×L协方差描述。该模型在混合近场（NF）和远场接入场景中变得不充分，因为近场设备引入设备特定的结构化空间协方差，且聚合观测不再符合远场快照结构。本文开发了一个统一的协方差感知Rician框架用于活动检测，通过单一似然模型处理远场和近场设备。在此框架内，我们提出了一种majorization-minimization投影梯度下降（MM-PGD）检测器用于处理得到的松弛似然。为了实现可扩展的精确似然评估，我们进一步推导了一种Kronecker-Woodbury实现，该实现利用混合近场/远场协方差结构，并避免对完整的LM×LM协方差矩阵进行分解。数值结果表明，在全远场极限下，MM-PGD与最强的逐坐标基线相当，而在测试场景中，随着近场用户比例的增加，其优势变得更加明显。
英文摘要
Grant-free random access in massive machine-type communications requires detecting a small active set from length-L uplink pilots received at an M-antenna base station. Classical covariance-based detectors are largely built on the far-field (FF) model, where the M antenna-domain observations are treated as independent snapshots, and inference reduces to an L $\times$ L covariance description. This model becomes inadequate in mixed near-field (NF) and FF access, where NF devices induce device-specific structured spatial covariances and the aggregate observation no longer fits the FF snapshot structure. In this paper, we develop a unified covariance-aware Rician framework for activity detection that treats FF and NF devices by a single likelihood model. Within this framework, we propose a majorization-minimization projected gradient descent (MM-PGD) detector for the resulting relaxed likelihood. For scalable exact likelihood evaluation, we further derive a Kronecker-Woodbury implementation that exploits the mixed NF/FF covariance structure, and avoids factorizing the full LM $\times$ LM covariance matrix. Numerical results show that MM-PGD is on par with the strongest coordinate-wise baseline in the all-FF limit, while its advantage becomes more pronounced as the fraction of NF users increases under the tested regimes.
发表机构
- Aalto University（阿尔托大学）
- Zhejiang University（浙江大学）
- Southern Illinois University（南伊利诺伊大学）
- Villanova University（维拉诺瓦大学）
机构由 AI 辅助整理，请以论文原文为准。
