Joint User Identification and Channel Estimation via Exploiting Spatial Channel Covariance in mMTC

Djelouat, Hamza; Leinonen, Markus; Ribeiro, Lucas Eduardo; Juntti, Markku

doi:10.1109/lwc.2021.3049167

Cited by 26 publications

(24 citation statements)

References 18 publications

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“…This paper is in line with our recent work [25], [26] where we addressed the JUICE under spatially correlated highly directive channels. In [25], [26], the JUICE was formulated as a mixed-norm minimization problem, augmented by a deterministic penalty that exploits the second-order statistics of the channels. In this paper, we further leverage the available knowledge on the entire CDI and treat the JUICE problem under a more rigorous, Bayesian framework.…”

Section: B Main Contributionsupporting

confidence: 86%

“…The optimization problem ( 25) is block multi-convex, i.e., the problem is convex in one set of variables while all the other variables are fixed. Since ADMM exploits implicitly the block multi-convexity nature of (25), utilizing ADMM to solve ( 25) is a reasonable choice. Accordingly, the augmented Lagrangian associated with ( 25) is given by…”

Section: B Map-admm Solutionmentioning

confidence: 99%

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Spatial Correlation Aware Compressed Sensing for User Activity Detection and Channel Estimation in Massive MTC

Djelouat

Leinonen

Juntti

2022

IEEE Trans. Wireless Commun.

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Grant-free access is considered as a key enabler for massive machine-type communications (mMTC) as it promotes energy-efficiency and small signalling overhead. Due to the sporadic user activity in mMTC, joint user identification and channel estimation (JUICE) is a main challenge. This paper addresses the JUICE in single-cell mMTC with single-antenna users and a multi-antenna base station (BS) under spatially correlated fading channels. In particular, by leveraging the sporadic user activity, we solve the JUICE in a multi measurement vector compressed sensing (CS) framework under two different cases, with and without the knowledge of prior channel distribution information (CDI) at the BS. First, for the case without prior information, we formulate the JUICE as an iterative reweighted 2,1-norm minimization problem. Second, when the CDI is known to the BS, we exploit the available information and formulate the JUICE from a Bayesian estimation perspective as a maximum a posteriori probability (MAP) estimation problem. For both JUICE formulations, we derive efficient iterative solutions based on the alternating direction method of multipliers (ADMM). The numerical experiments show that the proposed solutions achieve higher channel estimation quality and activity detection accuracy with shorter pilot sequences compared to existing algorithms.

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Section: B Main Contributionsupporting

confidence: 86%

Section: B Map-admm Solutionmentioning

confidence: 99%

Spatial Correlation Aware Compressed Sensing for User Activity Detection and Channel Estimation in Massive MTC

Djelouat

Leinonen

Juntti

2022

IEEE Trans. Wireless Commun.

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“…This paper is in line with our recent work [24], [25] where we addressed the JUICE under spatially correlated highly directive channels. In [24], [25], the JUICE was formulated as a mixednorm minimization problem, augmented by a deterministic penalty that exploits the second-order statistics of the channels. In this paper, we further leverage the available knowledge on the entire CDI and treat the JUICE problem under a more rigorous, Bayesian framework.…”

Section: B Main Contributionsupporting

confidence: 86%

Spatial Correlation Aware Compressed Sensing for User Activity Detection and Channel Estimation in Massive MTC

Djelouat¹,

Leinonen²,

Juntti³

2021

Preprint

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Grant-free access is considered as a key enabler to massive machine-type communications (mMTC) as it promotes energy-efficiency and small signalling overhead. Due to the sporadic user activity in mMTC, joint user identification and channel estimation (JUICE) is a main challenge. This paper addresses the JUICE in single-cell mMTC with single-antenna users and a multi-antenna base station (BS) under spatially correlated fading channels. In particular, by leveraging the sporadic user activity, we solve the JUICE in a multi measurement vector compressed sensing (CS) framework under two different cases, with and without the knowledge of prior channel distribution information (CDI) at the BS. First, for the case without prior information, we formulate the JUICE as an iterative reweighted 2,1 -norm minimization problem. Second, when the CDI is known to the BS, we exploit the available information and formulate the JUICE from a Bayesian estimation perspective as a maximum a posteriori probability (MAP) estimation problem. For both JUICE formulations, we derive efficient iterative solutions based on the alternating direction method of multipliers (ADMM). The numerical experiments show that the proposed solutions achieve higher channel estimation quality and activity detection accuracy with shorter pilot sequences compared to existing algorithms.

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“…complex Gaussian random variable with zero mean and E{|α n,s,l | 2 } = 1. In (2), the large-scale propagation effects and the BS antenna gain in the channel are captured in β n,s,l ∈ R, which 1 This assumption can be invoked by the fact that there exists a multitude of compressed sensing based approaches, e.g., [13]- [15], to solve the UE activity detection problem. However, this is outside of the scope of this paper and is left for future work.…”

Section: System Modelmentioning

confidence: 99%

Exploiting Spatial Correlation for Pilot Reuse in Single-Cell mMTC

Ribeiro

Leinonen

Al-Tous

et al. 2021

2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

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As a key enabler for massive machine-type communications (mMTC), spatial multiplexing relies on massive multiple-input multiple-output (mMIMO) technology to serve the massive number of user equipments (UEs). To exploit spatial multiplexing, accurate channel estimation through pilot signals is needed. In mMTC systems, it is impractical to allocate a unique orthogonal pilot sequence to each UE as it would require too long pilot sequences, degrading the spectral efficiency. This work addresses the design of channel features from correlated fading channels to assist the pilot assignment in multi-sector mMTC systems under pilot reuse of orthogonal sequences. In order to reduce pilot collisions and to enable pilot reuse, we propose to extract features from the channel covariance matrices that reflect the level of orthogonality between the UEs channels. Two features are investigated: covariance matrix distance (CMD) feature and CMD-aided channel charting (CC) feature. In terms of symbol error rate and achievable rate, the CC-based feature shows superior performance than the CMD-based feature and baseline pilot assignment algorithms.

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Joint User Identification and Channel Estimation via Exploiting Spatial Channel Covariance in mMTC

Cited by 26 publications

References 18 publications

Spatial Correlation Aware Compressed Sensing for User Activity Detection and Channel Estimation in Massive MTC

Spatial Correlation Aware Compressed Sensing for User Activity Detection and Channel Estimation in Massive MTC

Spatial Correlation Aware Compressed Sensing for User Activity Detection and Channel Estimation in Massive MTC

Exploiting Spatial Correlation for Pilot Reuse in Single-Cell mMTC

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