Throughput Estimation with Noise Uncertainty for Cyclostationary Feature Detector in Cognitive Radio Network

Abstract

Cognitive Radio Networks (CRNs) are recognized as the enabling technology for improving the future bandwidth utilization. In CRNs secondary users are allowed to utilize the frequency bands of primary users when these bands are not currently being used. The secondary users are required to sense the radio frequency environment. The lower the probability of false alarm, the more chances the channel can be reused and the higher the achievable throughput for the secondary network. The main contribution of this paper is to formulate the sensing-throughput-noise uncertainty tradeoff for cyclostationary feature detection. Computer simulations have shown that for a 1 MHz channel, when the sensing duration is 2% of total time, the spectrum will get 99% probability of detection regardless of 50% noise uncertainty.