Efficient Massive Machine Type Communication (mMTC) via AMP

Journal article (2023)

Authors

M. Mohammadkarimi Signal Processing Systems -

Masoud Ardakani University of Alberta

Research Group

Signal Processing Systems () (TU Delft)

DOI: https://doi.org/10.1109/LWC.2023.3256300

Sparse recovery Multiuser detection Approximate message passing Bayesian MMSE denoiser Short packet

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Published Date

2023

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Microelectronics

Research Group

Signal Processing Systems

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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- Embargo expired in 13-09-2023