Sparse Blind Demixing for Low-Latency Wireless Random Access with Massive Connectivity

Abstract

Massive connectivity has become a critical requirement for Internet-of-Things (IoT) networks, where a large number of devices need to connect to an access-point sporadically. Moreover, low-latency communication and sporadic device traffic are essential to support intelligent services in IoT networks. In this paper, to support low-latency communication for massive devices with sporadic traffic, we present a sparse blind demixing to simultaneously detect the active devices and decode multiple source signals without a priori channel state information in multi- in-multi-out (MIMO) networks. To address the unique challenges of bilinear measurements and sporadic device activity detection, we recast the estimation problem as a sparse and low-rank optimization problem via matrix lifting. We further propose a difference-of-convex-functions (DC) representation for the rank function to guarantee the exact rank constraint, followed by ignoring the non-convex group sparse function. This is achieved by exploiting the difference between nuclear norm and the convex Ky Fan k- norm for a rank function representation. We then develop an efficient DC algorithm to solve the resulting non-convex DC program without regularization parameter. Numerical results demonstrate that the proposed DC approach is able to exactly recover the ground truth signals with reduced sample sizes, as well as achieve better performance against noise compared with the existing convex methods.