Dictionary Learning Based Channel Estimation and Activity Detection for mMTC with Massive MIMO

Wireless random access (RA) faces huge challenges under the explosive growth of the Internet of Things devices to be connected to the base station. This leads to inevitable RA collisions. In this paper, we consider the RA in massive multiple-input multiple-output (MIMO) systems, and propose an activity detection and channel estimation algorithm based on dictionary learning. By exploiting the sporadic feature of massive connected devices that a small fraction of them being active, we exploit compressive sensing for simultaneous channel estimation and activity detection. More importantly, the proposed algorithm utilizes dictionary learning to abstract the sparse characteristics of the spatial channel in the massive MIMO system. A dictionary is learned by using historical channel information of each cell, and thus is appropriate for the specific cell. Simulation results demonstrate the superiority of the proposed method compared with existing methods.