Grant-free random access is a key enabler in massive machine-type communications (mMTC) to reduce signalling overhead and latency thereby improving the energy efficiency. One of its main challenges lies in joint user activity identification and channel estimation (JUICE). Due to the sporadic mMTC traffic, JUICE can be solved as a compressive sensing (CS) problem. We address CS-based JUICE in uplink with single-antenna transmitters and a multiantenna base station under spatially correlated fading channels. We formulate a novel CS problem that utilizes prior information on the second order statistics of the channel of each user to improve the performance. We propose a method based on alternating direction method of multipliers to solve the JUICE efficiently. The simulation results show that the proposed method significantly improves the user identification accuracy and channel estimation performance with lower signalling overhead as compared to the baseline schemes.

中文翻译：

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通过利用mMTC中的空间通道协方差进行联合用户识别和通道估计

无授权随机访问是大规模机器类型通信（mMTC）的关键推动力，它可以减少信令开销和等待时间，从而提高能效。其主要挑战之一在于联合用户活动识别和渠道估计（JUICE）。由于零星的mMTC流量，JUICE可以作为压缩感测（CS）问题解决。我们在空间相关的衰落信道下，通过单天线发射机和多天线基站在上行链路中解决基于CS的JUICE问题。我们提出了一个新颖的CS问题，该问题利用了有关每个用户频道的二阶统计信息的先验信息来提高性能。我们提出了一种基于乘法器交替方向方法的方法，可以有效地求解JUICE。