Joint Message Detection, Channel, and User Position Estimation for Unsourced Random Access in Cell-Free Networks

TL;DR

This paper introduces a joint detection and estimation method for unsourced random access in cell-free networks, leveraging location-based codebooks and AMP algorithms to improve user detection, channel, and position estimation.

Contribution

It proposes a novel location-partitioned codebook scheme combined with multisource AMP for joint message detection, channel, and position estimation in cell-free networks.

Findings

Excellent performance with perfect state evolution agreement

Significant outperformance over non-partitioned codebook baseline

Effective joint detection and estimation in distributed networks

Abstract

We consider unsourced random access (uRA) in user-centric cell-free (CF) wireless networks, where random access users send codewords from a common codebook during specifically dedicated random access channel (RACH) slots. The system is conceptually similar to the so-called 2-step RACH currently discussed in 3GPP standardization. In order to cope with the distributed and CF nature of the network, we propose to partition the network coverage area into zones (referred to as ''locations'') and assign an uRA codebook to each location, such that users in a certain location make use of the associated codebook. The centralized uRA decoder makes use of the multisource AMP algorithm recently proposed by the authors. This yields at once the list of active uRA codewords, an estimate of the corresponding channel vectors, and an estimate of the active users' position. We show excellent performance of this approach and perfect agreement with the rigorous theoretical ''state evolution'' analysis. We also show that the proposed ''location-based'' partitioned codebook approach significantly outperforms a baseline system with a single non-partitioned uRA codebook.