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

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.

16 pages, 11 figures

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• Sparse Activity Detection in Multi-Cell Massive MIMO Exploiting Channel Large-Scale Fading
• Performance Analysis of Joint Active User Detection and Channel Estimation for Massive Connectivity
• Massive Access for 5G and Beyond
• Structured Massive Access for Scalable Cell-Free Massive MIMO Systems
• Massive Access in Multi-cell Wireless Networks Using Reed-Muller Codes
• Massive Access in Cell-Free Massive MIMO-Based Internet of Things: Cloud Computing and Edge Computing Paradigms
• Covariance-Based Cooperative Activity Detection for Massive Grant-Free Random Access
• Joint Activity Detection and Channel Estimation for mmW/THz Wideband Massive Access
• Design of Non-Orthogonal Sequences Using a Two-Stage Genetic Algorithm for Grant-Free Massive Connectivity
• Cooperative Activity Detection: Sourced and Unsourced Massive Random Access Paradigms
• Asynchronous Massive Access in Multi-cell Wireless Networks Using Reed-Muller Codes
• Exploiting Simultaneous Low-Rank and Sparsity in Delay-Angular Domain for Millimeter-Wave/Terahertz Wideband Massive Access
• Grant-Free Access via Bilinear Inference for Cell-Free MIMO with Low-Coherent Pilots
• Feature-Aided Adaptive-Tuning Deep Learning for Massive Device Detection
• A Bayesian Tensor Approach to Enable RIS for 6G Massive Unsourced Random Access