Efficient Massive Machine Type Communication (mMTC) via AMP

Efficient Massive Machine Type Communication (mMTC) via AMP

Mohammadkarimi, M. (TU Delft Signal Processing Systems)
Ardakani, Masoud (University of Alberta)

2023

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.

Multiuser detection
approximate message passing
Bayesian MMSE denoiser
sparse recovery
short packet

http://resolver.tudelft.nl/uuid:80b05876-ed04-44b9-828c-112327583571

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

2023-09-13

2162-2345

IEEE Wireless Communications Letters, 12 (6), 1002-1006

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2023 M. Mohammadkarimi, Masoud Ardakani