This paper proposes a grant-free massive access scheme based on the millimeter wave (mmWave) extra-large-scale multiple-input multiple-output (XL-MIMO) to support massive Internet-of-Things (IoT) devices with low latency, high data rate, and high localization accuracy in the upcoming sixth-generation (6G) networks. The XL-MIMO consists of multiple antenna subarrays that are widely spaced over the service area to ensure line-of-sight (LoS) transmissions. First, we establish the XL-MIMO-based massive access model considering the near-field spatial non-stationary (SNS) property. Then, by exploiting the block sparsity of subarrays and the SNS property, we propose a structured block orthogonal matching pursuit algorithm for efficient active user detection (AUD) and channel estimation (CE). Furthermore, different sensing matrices are applied in different pilot subcarriers for exploiting the diversity gains. Additionally, a multi-subarray collaborative localization algorithm is designed for localization. In particular, the angle of arrival (AoA) and time difference of arrival (TDoA) of the LoS links between active users and related subarrays are extracted from the estimated XL-MIMO channels, and then the coordinates of active users are acquired by jointly utilizing the AoAs and TDoAs. Simulation results show that the proposed algorithms outperform existing algorithms in terms of AUD and CE performance and can achieve centimeter-level localization accuracy.

翻译：本文提出了一种基于毫米波超大规模多输入多输出（XL-MIMO）的无授权大规模接入方案，旨在支持即将到来的第六代（6G）网络中具有低延迟、高数据速率和高定位精度需求的大规模物联网（IoT）设备。XL-MIMO由多个广泛分布于服务区域的天线子阵组成，以确保视距（LoS）传输。首先，我们建立了考虑近场空间非平稳（SNS）特性的XL-MIMO大规模接入模型。然后，通过利用子阵的块稀疏性和SNS特性，提出了一种结构化块正交匹配追踪算法，用于高效的有源用户检测（AUD）和信道估计（CE）。此外，在不同导频子载波上应用不同的感知矩阵以利用分集增益。同时，设计了一种多子阵协同定位算法。特别地，从估计的XL-MIMO信道中提取有源用户与相关子阵间视距链路的到达角（AoA）和到达时间差（TDoA），进而通过联合利用AoA和TDoA获取有源用户坐标。仿真结果表明，所提算法在AUD和CE性能方面优于现有算法，并可实现厘米级定位精度。