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This paper proposes a grant-free massive access scheme based on themillimeter 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 arewidely spaced over the service area to ensure line-of-sight (LoS)transmissions. First, we establish the XL-MIMO-based massive access modelconsidering the near-field spatial non-stationary (SNS) property. Then, byexploiting the block sparsity of subarrays and the SNS property, we propose astructured block orthogonal matching pursuit algorithm for efficient activeuser detection (AUD) and channel estimation (CE). Furthermore, differentsensing matrices are applied in different pilot subcarriers for exploiting thediversity gains. Additionally, a multi-subarray collaborative localizationalgorithm is designed for localization. In particular, the angle of arrival(AoA) and time difference of arrival (TDoA) of the LoS links between activeusers and related subarrays are extracted from the estimated XL-MIMO channels,and then the coordinates of active users are acquired by jointly utilizing theAoAs and TDoAs. Simulation results show that the proposed algorithms outperformexisting algorithms in terms of AUD and CE performance and can achievecentimeter-level localization accuracy.

Sensing User's Activity, Channel, and Location with Near-Field Extra-Large-Scale MIMO