Efficient and low complexity optimized feature spectrum sensing with receiver offsets

Abstract

Spectrum awareness is an important function in the context of cognitive radio systems. It determines the presence or absence of free channels in the spectrum and identifies free channels for secondary users. Cyclostationary Feature Detection is an example of a spectrum awareness technique which involves the detection of signals based on their features such as cyclic frequencies, symbol rates, carrier frequencies and modulation types. It detects signals at very low signal-to-noise ratios. However there are performance degrading constraints such as cyclic and sampling clock offsets that can occur at the receiver end. These offsets result from local oscillator frequency offsets, Doppler effects and jitter. We propose an efficient low complexity multi-slot cyclostationary feature detector that reduces the effects of these constraints through an offline optimization approach that produces the number and size of slot and fast Fourier transform to be used. These slots and fast Fourier transforms are used to show the reduction of these offsets and the detection performance compared for the case of different signal to noise ratios in the presence or absence of the receiver offsets. Also, the complexity of the model is compared with the complexity of the conventional implementation and it shows significant reductions in the number of required computations.