In-band Spectrum Sensing in Cognitive Radio Networks: Energy Detection or Feature Detection?

Abstract

In a cognitive radio network (CRN), in-band spectrum sensing is essential for the protection of legacy spectrum users, with which the presence of primary users (PUs) can be detected promptly, allowing secondary users (SUs) to vacate the channels immediately. For in-band sensing, it is important to meet the detectability requirements, such as the maximum allowed latency of detection (e.g., 2 seconds in IEEE 802.22) and the probability of mis-detection and false-alarm. In this paper, we propose an efficient periodic in-band sensing algorithm that optimizes sensing-frequency and sensingtime by minimizing sensing overhead while meeting the detectability requirements. The proposed scheme determines the better of energy or feature detection that incurs less sensing overhead at each SNR level, and derives the threshold aRSSthreshold on the average received signal strength (RSS) of a primary signal below which feature detection is preferred. We showed that energy detection under lognormal shadowing could still perform well at the average SNR < SNRwall [1] when collaborative sensing is used for its location diversity. Two key factors affecting detection performance are also considered: noise uncertainty and inter-CRN interference. aRSSthreshold appears to lie between -114:6 dBm and -109.9 dBm with the noise uncertainty ranging from 0.5 dB to 2 dB, and between -112:9 dBm and -110:5 dBm with 1∼6 interfering CRNs.