Generalized Multi-user Sparse Superposition Transmission for Massive Machine-type Communications

-To fulfill the connectivity demands in massive machine-type communications (mMTC), this paper investigates a generalized multi-user sparse superposition transmission (GMUSST) technology based on position index modulation. Due to the high computation complexity of maximum likelihood (ML) multi-user detection, a low complexity multi-path successive interference cancellation (MSIC) multi-user detector is introduced to achieve near-ML detector's block error ratio (BLER) performance. Furthermore, considering that each user is only concerned with their own transmitted signal in the downlink GMUSST system, we propose a minimum mean square error based SIC (MMSE-SIC) detector, which can directly extract the user's transmission signal from the received superimposed signal of multiple users and is verified compared with MSIC detector. Simulation results show that the GMUSST can achieve better transmission reliability than the existing polar coded sparse code multiple access (PC-SCMA) in the short packet communication scenarios. Especially with the hybrid automatic repeat request mechanism, GMUSST requires fewer retransmissions to achieve the same BLER performance compared to PC-SCMA.