A Grant-Based Random Access Scheme With Low Latency for mMTC in IoT Networks

Abstract

The design of transmission schemes and receiver techniques with high reliability and low latency for the massive machine-type communications (mMTC) is an important challenge for the future Internet of Things (IoT) systems in the sixth-generation (6G) wireless communication networks. In this paper, we propose a new physical-layer transceiver scheme for mMTC, where users transmit their data based on a pre-designed sparse Tanner graph, and the base station (BS) employs blind channel estimation and message-passing decoding to decode user data. In particular, low latency is achieved by the construction of the transmission Tanner graph, as well as a hybrid modulation scheme that consists of BPSK symbols to facilitate blind channel estimation, and general M-ary modulation to reduce the transmission delay. Moreover, high reliability is achieved by the proposed message-passing decoder that fully exploits the diversity of the transmitted signal and offers soft demodulation and decoding capabilities. We provide both performance analyses based on density evolution, and simulation results, to demonstrate the superior performance of the proposed physical-layer transceiver solution for mMTC.