Efficient Massive Machine Type Communication (mMTC) via AMP

Journal Article (2023)
Author(s)

Mostafa Mohammadkarimi (TU Delft - Signal Processing Systems)

M. Ardakani (University of Alberta)

Research Group
Signal Processing Systems
Copyright
© 2023 M. Mohammadkarimi, Masoud Ardakani
DOI related publication
https://doi.org/10.1109/LWC.2023.3256300
More Info
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Publication Year
2023
Language
English
Copyright
© 2023 M. Mohammadkarimi, Masoud Ardakani
Research Group
Signal Processing Systems
Issue number
6
Volume number
12
Pages (from-to)
1002-1006
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Abstract

We propose efficient and low-complexity multiuser detection (MUD) algorithms for Gaussian multiple access channel (G-MAC) for short-packet transmission in massive machine type communications. To do so, we first formulate the G-MAC MUD problem as a sparse signal recovery problem and obtain the exact and approximate joint prior distribution of the sparse vector to be recovered. Then, we employ the Bayesian approximate message passing (AMP) algorithms with the optimal separable and non-separable minimum mean squared error (MMSE) denoisers for soft decoding of the sparse vector. The effectiveness of the proposed MUD algorithms for a large number of devices is supported by simulation results. For packets of 8 information bits, while the state-of-the-art AMP with soft-threshold denoising achieves 8/100 of the upper bound at Eb/N0 = 4 dB, the proposed algorithms reach 4/7 and 1/2 of the upper bound.

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