Spectrum sensing for wireless microphone signals using multiple antennas

Spectrum sensors for cognitive radio are expected to deploy multiple antennas to overcome the noise uncertainty problem and to minimize the effects of small-scale fading. Despite the requirement that these sensors must detect wireless microphone (WM) signals, several studies have focused either on general-purpose multiantenna detectors or on single-antenna WM detectors. We exploit the spatial structure and particular properties of WM waveforms to derive four multiantenna detectors for WM signals with different performance/complexity tradeoffs. These detectors are based on the generalized likelihood ratio (GLR) test, which is derived under several signal models exploiting either that the bandwidth of a WM signal never exceeds 200 kHz, that these signals have a constant magnitude (CM), or both. The proposed detectors do not require synchronization with the WM signal and are robust to the noise uncertainty problem and to small-scale fading. Using the simulation guidelines from the IEEE 802.22 standard, the novel multiantenna WM detectors are shown to outperform previous schemes, thus demonstrating the advantages of exploiting spatial correlation along with the WM signal structure.