Pilot-Based Unsourced Random Access with a Massive MIMO Receiver: Interference Cancellation and Power Control

TL;DR

This paper proposes a pilot-based massive MIMO approach for unsourced random access in Rayleigh fading channels, analyzing interference cancellation, power control, and a novel extension of the MMV-AMP algorithm for improved performance.

Contribution

It introduces a simple, energy-efficient pilot-based method with interference cancellation and a new MMV-AMP extension for handling deterministic unknowns in massive random access.

Findings

Closed-form performance approximation for pilot-based MRC approach

Interference cancellation with group SIC reduces transmit power

Power control policies improve system robustness

Abstract

In this work we treat the unsourced random access problem on a Rayleigh block-fading AWGN channel with multiple receive antennas. Specifically, we consider the slowly fading scenario where the coherence block-length is large compared to the number of active users and the message can be transmitted in one coherence block. Unsourced random access refers to a form of grant-free random access where users are considered to be a-priori indistinguishable and the receiver recovers a list of transmitted messages up to permutation. In this work we show that, when the coherence block length is large enough, a conventional approach based on the transmission of non-orthogonal pilot sequences with subsequent channel estimation and Maximum-Ratio-Combining (MRC) provides a simple energy-efficient solution whose performance can be well approximated in closed form. Furthermore, we analyse the MRC step when successive interference cancellation (SIC) is done in groups, which allows to strike a balance between receiver complexity and reduced transmit powers. Finally, we investigate the impact of power control policies taking into account the unique nature of massive random access, including short message lengths, uncoordinated transmission, a very large amount of concurrent transmitters with unknown identities, channel estimation errors and decoding errors. As a byproduct we also present an extension of the MMV-AMP algorithm which allows to treat pathloss coefficients as deterministic unknowns by performing maximum likelihood estimation in each step of the MMV-AMP algorithm.