Asynchronous Random Access in Massive MIMO Systems Facilitated by the Delay-Angle Domain
Ao Chen, Wei Chen, Bo Ai, Petar Popovski

TL;DR
This paper proposes a novel asynchronous random access scheme for massive MIMO systems that exploits delay and angle information to improve activity detection and channel estimation, addressing scalability issues in grant-free access.
Contribution
It introduces a cluster-extended sparse Bayesian learning algorithm that leverages delay-angle domain sparsity for enhanced asynchronous activity detection and channel estimation in massive MIMO.
Findings
Proposed method outperforms existing techniques in simulations.
Effective joint activity detection and channel estimation.
Utilizes delay and angle information for improved accuracy.
Abstract
The problem of uplink transmissions in massive connectivity is commonly dealt with using schemes for grant-free random access. When a large number of devices transmit almost synchronously, the receiver may not be able to resolve the collision. This could be addressed by assigning dedicated pilots to each user, leading to a contention-free random access (CFRA), which suffers from low scalability and efficiency. This paper explores contention-based random access (CBRA) schemes for asynchronous access in massive multiple-input multiple-output (MIMO) systems. The symmetry across the accessing users with the same pilots is broken by leveraging the delay information inherent to asynchronous systems and the angle information from massive MIMO to enhance activity detection (AD) and channel estimation (CE). The problem is formulated as a sparse recovery in the delay-angle domain. The challenge…
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Taxonomy
TopicsEnergy Harvesting in Wireless Networks · Advanced MIMO Systems Optimization · Wireless Body Area Networks
