Coexistence of Human-Type and Machine-Type Communications in Uplink Massive MIMO

Abstract

In this article, we study the receiver design for the uplink transmission of a human-type communications (HTC) and machine-type communications (MTC) (H&M) coexisted massive MIMO system. We first establish a probability model to characterize the crucial system features including channel sparsity of massive MIMO and signal sparsity of MTC packets. With the probability model, we propose to conduct joint device activity identification, channel estimation, and signal detection. We develop a message-passing-based statistical interference framework to systematically and efficiently solve the joint estimation problem for the H&M coexisted massive MIMO system. Specifically, we propose two receiver schemes based on time-slotted and non-time-slotted grant-free random access for massive machine-type device connectivity. We show that, by exploiting the channel and signal sparsity, our proposed message-passing-based algorithms significantly outperform the conventional training-based approaches in which the device activity state and the channel are estimated by sending pilots prior to data transmission, and are able to approach the genie bound with known signal support in the relatively high signal-to-noise (SNR) regime. Last but not least, we show that there exists a significant gain in terms of the number of admissible devices in the system by allowing H&M coexistence, as compared to orthogonal transmission approaches in which different time/frequency slots are assigned to HTC and MTC services.