Feature-based Spectrum Sensing of NOMA System for Cognitive IoT Networks

With the rapid increase of the demand for the Internet of Things (IoT), spectrum resources have incremental challenges. Nonorthogonal multiple access (NOMA) and spectrum sensing (SS) are considered key candidate technologies for next-generation wireless communications to improve spectrum utilization. Nevertheless, using both technologies at the same time makes the system more complex and brings new challenges to user differentiation. In order to make better use of these advantages, we creatively propose a feature detection-based SS method for NOMA systems. To better distinguish the relationship between the presence or absence of signals from different NOMA users, we employ feature detection to obtain the feature values of each user. We propose workflows and transceiver architectures combining the two technologies. Based on the relationship among users' priorities, power, and transmission in common scenarios, we design a downlink mode and two uplink modes and deduce the threshold settings of the corresponding modes. Meanwhile, we also customarily propose enhanced algorithms, to have a marked increase in the performance for the proposed method in various modes. Experimental results illustrate that the proposed technique is feasible and has prominent detection performance and satisfying throughput performance.