This paper investigates the unsourced random access (URA) problem with a massive multiple-input multiple-output receiver that serves wireless devices in the near-field of radiation. We employ an uncoupled transmission protocol without appending redundancies to the slot-wise encoded messages. To exploit the channel sparsity for block length reduction while facing the collapsed sparse structure in the angular domain of near-field channels, we propose a sparse channel sampling method that divides the angle-distance (polar) domain based on the maximum permissible coherence. Decoding starts with retrieving active codewords and channels from each slot. We address the issue by leveraging the structured channel sparsity in the spatial and polar domains and propose a novel turbo-based recovery algorithm. Furthermore, we investigate an off-grid compressed sensing method to refine discretely estimated channel parameters over the continuum that improves the detection performance. Afterward, without the assistance of redundancies, we recouple the separated messages according to the similarity of the users' channel information and propose a modified K-medoids method to handle the constraints and collisions involved in channel clustering. Simulations reveal that via exploiting the channel sparsity, the proposed URA scheme achieves high spectral efficiency and surpasses existing multi-slot-based schemes. Moreover, with more measurements provided by the overcomplete channel sampling, the near-field-suited scheme outperforms its counterpart of the far-field.

翻译：本文研究了一种使用大规模多输入多输出接收机服务近场辐射区内无线设备的无源随机接入问题。我们采用无冗余追加的时隙独立编码消息传输协议。针对近场信道在角度域呈现的塌缩稀疏结构，为通过信道稀疏性缩减块长度，提出了一种基于最大允许相干性的角度-距离（极坐标）域稀疏信道采样方法。解码过程始于从每个时隙中提取活跃码字与信道信息，通过利用空间域与极坐标域的结构化信道稀疏性解决该问题，并提出了一种基于Turbo的新型恢复算法。进一步地，我们研究了一种离网格压缩感知方法，在连续域上对离散估计的信道参数进行精化，从而提升检测性能。随后，在不借助冗余辅助的情况下，根据用户信道信息的相似性重新关联分离的消息，并提出一种改进的K-medoids方法处理信道聚类中的约束与冲突。仿真表明，通过利用信道稀疏性，所提无源随机接入方案实现了高频谱效率，并超越了现有基于多时隙的方案。此外，由于过完备信道采样提供了更多测量值，适用于近场的方案在性能上优于远场对应方案。