Intelligent DSA-assisted clustered IoT networks: neuromorphic computing meets genetic algorithm

Abstract

Dynamic spectrum access (DSA) is a promising technology to increase the spectrum efficiency of Internet of Things (IoT) networks, where the traffic demand grows up dramatically recently. In this paper, an intelligent DSA-assisted IoT network is introduced, where we investigate the spectrum sensing through neuromorphic computing (NC) and spectrum access through genetic algorithm (GA)-based power allocation. To be specific, we apply the NC's unconventional computing architectures that exploit and harness the intrinsic dynamics for computation, and thus provide increased functionality with better spectrum sensing performance requiring significantly lower size, weight, and power budgets. Furthermore, we design a GA algorithm to intelligently search the desirable transmission power for multiple IoT devices sharing the same channel to enhance the capacity of the highly dynamic DSA-assisted IoT network. Extensive simulation results have demonstrated the benefits of NC and GA compared to other baseline algorithms and methodologies.