Performance Evaluation for Grant-Free NOMA System Using Hybrid SCMA-OFDM

Grant-free non-orthogonal multiple access (GF-NOMA) technology shows promise in serving massive machine-type communication (mMTC) Internet-of-Thing (IoT) users, thanks to its low signaling and transmission overhead. Nonetheless, GF-NOMA technology suffers from severe collisions due to the absence of grants, leading to incorrect decoding and transmission inefficiency. To reduce collisions in the GF-NOMA system, we propose a hybrid framework that incorporates Sparse Code Multiple Access (SCMA) in the power domain non-orthogonal multiple access (PD-NOMA) system. This framework classifies users into two different groups based on their power levels. One group of users employs SCMA, distinguishing themselves from each other using their respective codebooks. The other group consists of traditional users utilizing orthogonal frequency-division multiplexing (OFDM). By adopting this approach, we can utilize the same amount of spectrum resources as in traditional PD-NOMA while increasing the number of contention transmission units (CTUs) to reduce the probability of collisions. Simulation results indicate that our proposed framework achieves a higher decoding success rate compared to traditional PD-NOMA.