Device Activity Detection and Non-Coherent Information Transmission for Massive Machine-Type Communications

Abstract

In the grant-free massive machine-type communication (mMTC) scenario, a key challenge is the joint device activity detection and data decoding. The sporadic nature of mMTC makes compressed sensing a promising solution to the activity detection problem. However, the typical two-phase coherent transmission scheme, which divides channel training and data decoding into two separate phases, suffers performance losses, especially when only a few bits of data are transmitted by each active device. This paper follows a newly proposed non-coherent transmission scheme in which the data bits are embedded in the pilot sequences and the BS simultaneously detects active devices and decodes the embedded data bits without explicit channel estimation. To exploit statistical channel information and the specific structure of the sparsity pattern introduced by the non-coherent transmission scheme, i.e., only one row in each section can be non-zero, we propose a receiving method based on the approximate message passing (AMP) algorithm with non-separable minimum mean-squared error denoisers specifically designed for the problem. The corresponding state evolution equations, which can be used to predict the section error rate (SER) performance, is obtained and simplified under certain assumptions. We also derive closed-form expressions of the SER performance based on the state evolution results. Finally, numerical simulations are given to validate the accuracy of the performance analysis and to show the superiority of the proposed receiving method over the conventional method based on AMP with separable denoisers in the literature.

Keywords

• Massive machine-type communication (mMTC)
• non-coherent transmission
• grant-free NOMA
• approximate message passing (AMP)
• non-separable denoiser
• state evolution