Non-Bayesian Activity Detection, Large-Scale Fading Coefficient Estimation, and Unsourced Random Access with a Massive MIMO Receiver
Alexander Fengler, Saeid Haghighatshoar, Peter Jung, Giuseppe Caire

TL;DR
This paper demonstrates that massive MIMO systems can effectively detect user activity and estimate fading coefficients in unsourced random access scenarios, overcoming previous compressed sensing limitations through large antenna arrays.
Contribution
It introduces new algorithms for activity detection and fading estimation that work with more users than available signal dimensions, leveraging large MIMO arrays.
Findings
Reliable detection at any SNR with enough antennas
Achieves spectral efficiency of order L log L
Overcomes compressed sensing limitations in massive access
Abstract
In this paper, we study the problem of user activity detection and large-scale fading coefficient estimation in a random access wireless uplink with a massive MIMO base station with a large number of antennas and a large number of wireless single-antenna devices (users). We consider a block fading channel model where the -dimensional channel vector of each user remains constant over a coherence block containing signal dimensions in time-frequency. In the considered setting, the number of potential users is much larger than but at each time slot only of them are active. Previous results, based on compressed sensing, require that , which is a bottleneck in massive deployment scenarios such as Internet-of-Things and unsourced random access. In this work we show that such limitation can be overcome when the number of base station…
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