Distributed Massive MIMO With Low Resolution ADCs for Massive Random Access

Massive machine-type communications (mMTC), an essential fifth-generation (5G) usage scenario, aims to provide services for a large number of users that intermittently transmit small data packets in smart cities, manufacturing, and agriculture. Massive random access (MRA) emerges as a promising candidate for multiple access in mMTC characterized by the sporadic data traffic. Despite the use of massive multiple-input multiple-output (mMIMO) in MRA to achieve spatial division multiple access and mitigate small-scale fading, existing research endeavors overlook the near-far effect of large-scale fading by assuming perfect power control. In this paper, we present a cost-efficient, effective, and fully distributed solution for MRA to combat large-scale fading, wherein distributed access points (APs) cooperatively detect and serve active users. Each AP is equipped with low resolution analog-to-digital converters (ADCs) for energy-efficient system implementation. Specifically, we derive a rigorous closed-form expression for the uplink achievable rate, considering the impact of non-orthogonal pilots and low resolution ADCs. We also propose a scalable distributed algorithm for user activity detection under flat fading channels, and further adapt it to handle frequency-selective fading in popular orthogonal frequency division multiplexing (OFDM) systems. The proposed solution is fully distributed, since most processing tasks, such as activity detection, channel estimation, and data detection, are localized at each AP. Simulation results demonstrate the significant advantage of distributed systems over co-located systems in accommodating more users while achieving higher activity detection accuracy, and quantify performance loss resulting from the use of low resolution ADCs.