Channel Estimation and User Activity Identification in Massive Grant-Free Multiple-Access

Abstract

Grant-free multiple-access (GFMA) allows to significantly reduce the overhead of granted multiple-access. However, information detection in GFMA is challenging, as it has to be executed along with the activity detection of user equipments (UEs) and channel estimation. In this paper, we study the channel estimation and propose the UE activity identification (UAI) algorithms for the massive connectivity supporting GFMA (mGFMA) systems. For these purposes, the channel estimation is studied from several aspects by assuming different levels of knowledge to the access point, and based on which five UAI approaches are proposed. We study the performance of channel estimation, the statistics of estimated channels, and the performance of UAI algorithms. Our studies show that the proposed approaches are capable of circumventing some of the shortcomings of the existing techniques designed based on compressive sensing and message passing algorithms. They are robust for operation in the mGFMA systems where the active UEs and the number of them are highly dynamic.