Massive Unsourced Random Access Based on Bilinear Vector Approximate Message Passing

Ayachi, Ramzi; Akrout, Mohamed; Shyianov, Volodymyr; Bellili, Faouzi; Mezghani, Amine

doi:10.1109/icassp43922.2022.9747338

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…BCS relies on message-passing algorithms based on belief propagation (BP), expectation propagation (EP) or approximate message passing (AMP). In the case of independent sensors activity, a number of methods have been developed [5]- [10]. In [5], the simplest version of AMP has been used to perform AUDaCE.…”

Section: A Related Workmentioning

confidence: 99%

“…In [7], [8], hybrid GAMP (HGAMP) is used to perform AUDaCE with low-precision analog-to-digital converter, incorporating independent activity prior information for the active user detection. In [10], AUDaCE is accomplished using bilinear vector AMP in the context of massive unsourced RA.…”

Section: A Related Workmentioning

confidence: 99%

“…The compressed sensing (CS) algorithms in [5], [7], [8], [10], [11] rely on the assumption that the random sensor activities are mutually independent. However, when sensors monitor the same machines, events may be detected by multiple sensors, leading to correlated activity.…”

Section: A Related Workmentioning

confidence: 99%

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Active User Detection and Channel Estimation for Grant-Free Random Access with Gaussian Correlated Activity

Chetot,

Egan,

Gorce

2023

2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring)

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Industrial IoT (IIoT) is one of the major verticals targeted by the next generations of wireless networks. In order to provide industrial plants with features relying on wireless communications, the grant-free RA (GFRA) protocol appears to be a promising means for supporting massive ultra-reliable connectivity; at the same time, it is a critical bottleneck that requires an access point (AP) to be able to jointly perform active user detection and channel estimation (AUDaCE) to fulfill its main mission of allowing industrial wireless devices to access the core network. This mission is even harder when the GFRA requests are correlated because of event-driven activity triggers. This paper proposes a new tractable gaussian correlated activity (GCA) model for this scenario. The corresponding AUDaCE problem is then studied in the Bayesian compressed sensing (BCS) framework. An hybrid instance of the generalized AMP (GAMP) algorithm is derived and its capability to perform AUDaCE is numerically assessed by extensive Monte-Carlo simulations. The numerical results show gains of 2.5dB in channel estimation gain for twice less detection errors w.r.t. state-of-theart algorithms.

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Section: A Related Workmentioning

confidence: 99%