Maximizing Achievable Transmission Time in Cognitive Radio Networks Under Sensor-Aided Crowdsourced Spectrum Sensing

Abstract

Abstract Spectrum-sensing as a service has been proposed and studied by many researchers over the past decade as a promising approach to support the viability of cognitive radio networks (CRNs). A spectrum-sensing service provider (SSP) provides information about spectrum occupancy to its clients that is generally more accurate than what clients can learn on their own. Two approaches are used by SSPs in their operation, the dedicated sensing infrastructure approach (sensor-aided CRN) and the crowdsensing approach. In this work, we assume a hybrid model where a dedicated sensing infrastructure is used along with crowdsensing. We study the tradeoff between sensing time paid by cognitive users to the SSP and their achievable transmission time. Our objective is to maximize the minimum achievable transmission time for any cognitive user in the network by carefully selecting the channels to be used. Two algorithms are proposed, one is based on the hill-climbing search algorithm (abbreviated HCA) and the other is a less optimal but faster greedy selection algorithm (abbreviated GSA). Results show that both HCA and GSA are within 3% of the optimal solution. Results also confirm that GSA is faster than HCA, while HCA outperforms GSA.