Modeling, Analysis, and Optimization of Grant-Free NOMA in Massive MTC via Stochastic Geometry

In the massive machine-type communications (mMTCs) scenario for Internet-of-Things (IoTs) applications, though a large number of devices are registered to an access point (AP), very few them active with uplink short packet transmission simultaneously, which requires novel design protocols and receivers enable efficient data accurate multiuser detection (MUD). Aiming at this problem, grant-free nonorthogonal multiple (GF-NOMA) is proposed, enables directly transmit their preambles symbols altogether within one time frame, without grant from AP. Compressive sensing (CS)-based adopted (NOPs)-based MUD, successive interference cancellation exploited decode superimposed signals. article, aspect network deployment, we model, analyze, optimize CS-based GF-MONA mMTC system via stochastic geometry (SG). Based on SG first analyze success probability as well channel estimation error MUD in preamble phase then average aggregate rate phase. We energy efficiency AP coverage GF-NOMA numerical methods meet low-energy-consumption low-infrastructure-cost demands IoT applications. Our analysis results verified Monte Carlo simulations, show that NOP yields better performances than contention-based orthogonal access.