Efficient Massive Machine Type Communication (mMTC) via AMP

TL;DR

This paper introduces low-complexity Bayesian AMP algorithms for efficient multiuser detection in massive machine type communications, significantly improving detection performance for short packets in G-MAC scenarios.

Contribution

It formulates G-MAC MUD as a sparse recovery problem and develops AMP algorithms with optimal MMSE denoisers tailored for short-packet mMTC, enhancing detection accuracy.

Findings

Proposed algorithms outperform state-of-the-art AMP in detection accuracy.

Achieve near upper-bound detection performance at low Eb/N0.

Support large-scale device connectivity with low complexity.

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.