Adaptive sensing cycle for multichannel cognitive radio networks based on packet statistics

Multichannel sensing scheduling and interference avoidance are two major challenges for medium access in Cognitive Radio Networks (CRN). In this paper, we propose an adaptive sensing cycle scheme for scheduling multichannel sensing with interference avoidance in CRN. The principle of the proposed adaptive sensing cycle scheme is to prioritize the multichannel sensing and access based on interference estimation from packet statistics. To keep fairness of media access within the CRN, the sensing cycle is formulated under the constrained of constant overall sensing rate for all nodes. Unlike conventional spectrum sensing in physical layer, the interference estimation is based on packet statistics in Media Access Control (MAC) layer and measures the statistical channel availability beyond instantaneous channel availability. Radio performance is studied in a test-bed environment under various interference intensities: the results show that more than 45% throughput improvement is achieved in interference-intensive environments.