H. Djelouat, L. Marata, M. Leinonen, H. Alves and M. Juntti, "User Activity Detection and Channel Estimation of Spatially Correlated Channels via AMP in Massive MTC," 2021 55th Asilomar Conference on Signals, Systems, and Computers, 2021, pp. 1200-1204, doi: 10.1109/IEEECONF53345.2021.9723198.
User activity detection and channel estimation of spatially correlated channels via AMP in massive MTC
|Author:||Djelouat, Hamza1; Marata, Leatile1; Leinonen, Markus1;|
1Centre for Wireless Communications – Radio Technologies, University of Oulu, Finland
|Online Access:||PDF Full Text (PDF, 0.3 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe202301031241
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2023-01-03
This paper addresses the problem of joint user identification and channel estimation (JUICE) for grant-free access in massive machine-type communications (mMTC). We consider the JUICE under a spatially correlated fading channel model as that reflects the main characteristics of the practical multiple-input multiple-output channels. We formulate the JUICE as a sparse recovery problem in a multiple measurement vector setup and present a solution based on the approximate message passing (AMP) algorithm that takes into account the channel spatial correlation. Using the state evolution, we provide a detailed theoretical analysis on the activity detection performance of AMP-based JUICE by deriving closed-from expressions for the probabilities of miss detection and false alarm. The simulation experiments show that the performance predicted by the theoretical analysis matches the one obtained by the numerical results.
Asilomar Conference on Signals, Systems & Computers
|Pages:||1200 - 1204|
Conference Record of The Fifty-Fifth Asilomar Conference on Signals, Systems & Computers
Asilomar Conference on Signals, Systems, and Computers
|Type of Publication:||
A4 Article in conference proceedings
|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 340171 and 323698). H. Djelouat would like to acknowledge the support of Tauno Tönning Foundation, Riitta ja Jorma J. Takanen Foundation, and Nokia Foundation. L. Marata’s work is supported partially by Botswana International University of Science and Technology.
|Academy of Finland Grant Number:||
319485 (Academy of Finland Funding decision)
318927 (Academy of Finland Funding decision)
340171 (Academy of Finland Funding decision)
323698 (Academy of Finland Funding decision)
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