A Dimension Reduction-Based Joint Activity Detection and Channel Estimation Algorithm for Massive Access

TL;DR

This paper introduces a dimension reduction-based joint activity detection and channel estimation algorithm for massive IoT access, significantly reducing computational complexity and pilot sequence length in large-scale MIMO systems.

Contribution

It proposes a novel dimension reduction method combined with a Riemannian trust-region algorithm to efficiently solve the non-convex JADCE problem in massive access scenarios.

Findings

Requires shorter pilot sequences than existing algorithms

Efficiently handles large-scale JADCE problems

Outperforms state-of-the-art methods in simulations

Abstract

Grant-free random access is a promising protocol to support massive access in beyond fifth-generation (B5G) cellular Internet-of-Things (IoT) with sporadic traffic. Specifically, in each coherence interval, the base station (BS) performs joint activity detection and channel estimation (JADCE) before data transmission. Due to the deployment of a large-scale antennas array and the existence of a huge number of IoT devices, JADCE usually has high computational complexity and needs long pilot sequences. To solve these challenges, this paper proposes a dimension reduction method, which projects the original device state matrix to a low-dimensional space by exploiting its sparse and low-rank structure. Then, we develop an optimized design framework with a coupled full column rank constraint for JADCE to reduce the size of the search space. However, the resulting problem is non-convex and highly intractable, for which the conventional convex relaxation approaches are inapplicable. To this end, we propose a logarithmic smoothing method for the non-smoothed objective function and transform the interested matrix to a positive semidefinite matrix, followed by giving a Riemannian trust-region algorithm to solve the problem in complex field. Simulation results show that the proposed algorithm is efficient to a large-scale JADCE problem and requires shorter pilot sequences than the state-of-art algorithms which only exploit the sparsity of device state matrix.