Device Detection and Channel Estimation in MTC with Correlated Activity Pattern

Hamza Djelouat; Mikko J. Sillanpää; Markku Juntti

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arXiv · 2310.14578

Device Detection and Channel Estimation in MTC with Correlated Activity Pattern

Hamza Djelouat·Mikko J. Sillanpää·Markku Juntti

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Abstract

This paper provides a solution for the activity detection and channel estimation problem in grant-free access with correlated device activity patterns. In particular, we consider a machine-type communications (MTC) network operating in event-triggered traffic mode, where the devices are distributed over clusters with an activity behaviour that exhibits both intra-cluster and inner-cluster sparsity patterns. Furthermore, to model the network's intra-cluster and inner-cluster sparsity, we propose a structured sparsity-inducing spike-and-slab prior which provides a flexible approach to encode the prior information about the correlated sparse activity pattern. Furthermore, we drive a Bayesian inference scheme based on the expectation propagation (EP) framework to solve the JUICE problem. Numerical results highlight the significant gains obtained by the proposed structured sparsity-inducing spike-and-slab prior in terms of both user identification accuracy and channel estimation performance.

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BibTeX RIS

Hamza Djelouat, Mikko J. Sillanpää, Markku Juntti. 2023-10-23. Device Detection and Channel Estimation in MTC with Correlated Activity Pattern. https://arxiv.org/abs/2310.14578

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