H. Djelouat, M. Leinonen and M. Juntti, "Spatial Correlation Aware Compressed Sensing for User Activity Detection and Channel Estimation in Massive MTC," in IEEE Transactions on Wireless Communications, vol. 21, no. 8, pp. 6402-6416, Aug. 2022, doi: 10.1109/TWC.2022.3149111
Spatial correlation aware compressed sensing for user activity detection and channel estimation in massive MTC
|Author:||Djelouat, Hamza1; Leinonen, Markus1; Juntti, Markku1|
1Centre for Wireless Communications – Radio Technologies, FI-90014, University of Oulu, Finland
|Online Access:||PDF Full Text (PDF, 1.3 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2022041929435
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2022-04-19
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 ℓ2,1-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.
IEEE transactions on wireless communications
|Pages:||6402 - 6416|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work has been financially supported in part by the Academy of Finland
(grant 319485) and Academy of Finland 6Genesis Flagship (grant 318927).
The work of M. Leinonen has also been financially supported in part by Infotech Oulu and the Academy of Finland (grant 323698). H. Djelouat would like to acknowledge the support of Tauno Tönning Foundation, Riitta ja Jorma J. Takanen Foundation, and Nokia Foundation.
|Academy of Finland Grant Number:||
323698 (Academy of Finland Funding decision)
318927 (Academy of Finland Funding decision)
319485 (Academy of Finland Funding decision)
© The Author(s) 2022. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.