User Activity Detection-Based Large SCMA System for Uplink Grant-Free Access

Sparse code multiple access (SCMA) is a promising non-orthogonal multiple access (NOMA) technology for next generation wireless networks to achieve higher spectral efficiency and lower latency in massive machine type communication systems. In the massive uplink grant-free communications, users can conduct uplink instant transmissions without grant to reduce signaling overhead as well as the latency. Multiusers' data symbols are detected using the message passing algorithm (MPA) aided by the exact a priori information of the active users. However, prior knowledge of the active users is impractical in a grant-free communication system due to randomness in users' activity. Therefore, in this paper, we propose a compressive sensing based SCMA system for grant-free massive machine type communications. In the proposed large SCMA system, the active users are detected using a two-phase procedure and then MPA-based receiver is adopted for active users' data symbol detection. The ability of user detection, false detection probability, and bit error rate performances are analyzed over additive white Gaussian noise (AWGN) and Rayleigh fading channels. The corresponding results provide useful insight to design a large SCMA system in situations such as massive machine type communications where prior user-activity information is not readily available.