Massive Uncoordinated Access with Massive MIMO: A Dictionary Learning Approach

Massive machine-type communications (mMTC) or massive Internet of things (IoT) is one of the key application scenarios of fifth generation (5G) and beyond cellular networks. Since initial access procedures of legacy systems are not suitable for massive connectivity due to increased collision probability and prohibitive overhead, it is of great interest to design an efficient grant-free access protocol. In this paper, we propose an uncoordinated access protocol for massive connectivity, which leverages a massive number of antennas at the base station (BS), which is expected to be widely deployed in cellular networks. With the proposed scheme consisting of a sparse frame structure and receiver processing based on sparse dictionary learning, a massive number of IoT devices can transmit data without any prior scheduling process. Unlike existing schemes that necessitate significant overhead for preamble signals, the overhead required for the proposed scheme is negligible, which is attractive in terms of resource utilization and transmit power consumption. Numerical results verify that the proposed scheme can be utilized to facilitate massive connectivity in realistic cellular-based IoT scenarios.