Joint Activity Detection and Channel Estimation for Massive Connectivity Network with 1-Bit DAC

Abstract

A key feature of the massive connectivity is the large number of potential low-cost users with sporadic user traffic. An important issue in massive connectivity is to identify the active users and estimate the channel of these users. Several works have studied the joint activity detection and channel estimation for massive connectivity network. However, these works consider the high-resolution digital-to-analog converter (DAC) for each user. Since the number of potential users is large, the hardware cost is prohibitive. Motivated by this, we investigate the massive connectivity network with L-bit DAC for each user. We propose a compressed sensing algorithm to identify the active users and estimate the channel. An analytical tool is presented to evaluate the performance of the algorithm. Interestingly, results show that the performance of 1-bit DAC scheme is only slightly inferior to the high-resolution DAC scheme of previous study.