Optimal Sensor Scheduling in Energy Harvesting-Aided Cognitive Radio Networks

Abstract

We consider the problem of scheduling of energy harvesting (EH)-based spectrum sensors for cognitive radio wireless sensor networks. These throughput-optimally scheduled EH sensors report their decisions to a fusion center, where an overall decision is made. To save on the reporting overhead, we consider the superior selective reporting (SSR) approach for a given channel, and a chosen set of spectrum sensors. Towards this end, we formulate an optimization problem to maximize the overall network throughput, subject to constraints on harvested energy and interference constraints on the primary network. Later, we carry out a numerical analysis, driven by experimental data captured through Realtek software defined radio platform (RTL-SDR) RTL2832U. For a comparative study, we discuss the utility of the SSR approach for scheduling in contrast to the conventional cooperative sensing approach and show that the SSR approach achieves a better throughput as the number of spectrum sensors increases. Hence, the SSR approach is a more appealing technology for cognitive networks envisioned for 5G communication systems.