Abstract
The successful operation of a cognitive radio system strongly depends on its ability to sense the radio environment. With the use of spectrum sensing algorithms, the cognitive radio is required to detect co-existing licensed primary transmissions and to protect them from interference. This paper focuses on filter-bank-based sensing and provides a solid theoretical background for the design of these detectors. Optimum detectors based on the Neyman-Pearson theorem are developed for uniform discrete Fourier transform (DFT) and modified DFT filter banks with root-Nyquist filters. The proposed sensing framework does not require frequency alignment between the filter bank of the sensor and the primary signal. Each wideband primary channel is spanned and monitored by several sensor subchannels that analyse it in narrowband signals. Filter-bank-based sensing is proved to be robust and efficient under coloured noise. Moreover, the performance of the weighted energy detector as a sensing technique is evaluated. Finally, based on the Locally Most Powerful and the Generalized Likelihood Ratio test, real-world sensing algorithms that do not require a priori knowledge are proposed and tested.
Highlights
Spectrum sensing has been brought into the center of research activities due to its application in the context of cognitive radio (CR) [1]
The cognitive radios share the available spectrum with a licensed primary system (PS) and have the responsibility not to adversely affect the PS user operation by causing interference
Given the fact that the wideband CR receiver should optimize the use of the limited computational resources, the analysis focuses on uniform discrete Fourier transform (DFT) [27] filter banks with downsampling at the Nyquist rate
Summary
Spectrum sensing has been brought into the center of research activities due to its application in the context of cognitive radio (CR) [1]. As in [14], filter banks are implemented using the polyphase structure in order to perform simultaneous parallel sensing on all subchannels; the proposed algorithms are not based on specific signal feature extraction. The designed sensors are based on the approach that there is no ‘1-1’ matching of the receiver filters and the PS signal bandwidth This is a common flaw among the majority of filter-bank-based sensing studies. The presented theoretical analysis assumes that the transmitted PS signal sn is circular white Gaussian random variable with mean power at the receiver (over a flat channel) σs. The presented theoretical analysis assumes that the transmitted PS signal sn is circular white Gaussian random variable with mean power at the receiver (over a flat channel) σs2 This assumption is accurate for orthogonal frequency division multiplexing (OFDM) signals, but it is questionable for single carrier signals.
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