Reliable Wideband Spectrum Sensing under Strong Interference Environments in Cognitive Radios

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Spectrum hole.png

Issues of low signal-to-noise ratio (SNR) and hidden terminal problems in a cognitive radio (CR) system are quite common and have been well addressed before. Most previous literature pursues more spatial diversity from cooperative networks to raise the detection probability and obtain more access opportunity. In the situation, the interference model for the CR system could be as similar as in the figure where the hidden PU is surrounded by strong interference (could be higher than 60 dB) at near frequency subbands. This could severely damage the detection performance (especially miss detection) even with a great sensing technique. Besides, to provide a more flexible and faster sensing, efficient wideband sensing methods instead of single band detection become necessary and have drawn much attention in these years.

In this project, the aim is to develop a reliable and efficient wideband spectrum sensing system that is applicable to the strong interference environments. (1) Wideband Sensing: The critical challenge for a wideband sensing system is to reliably identify available spectral holes with the required spectral resolution in an efficient way, both in system algorithm and in hardware architecture design. To solve these problems, an adaptive multitaper spectral detector has been introduced with the advantages of robust multitaper gains, highly adjustable spectra-resolution capability, and delicate weighting mechanism. (2) Hidden PUs detection: Hidden PUs detection under wideband sensing faces crucial sensing problems under "ultra low SNR" conditions, which is caused by colored interference, channel fading, and additive white Gaussian noise (AWGN). Rather accurate interference power estimation results (usually they are not achievable due to the uncertainty of noise or knowledge of interference) are required by conventional power spectrum- or energy-based detection methods. We are investigating fundamental statistical and spectral signal processing approaches to relieve the requirement.

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