Towards Faster Spectrum Sensing Techniques in Cognitive Radio Architectures

AbstractIn the recent years, the subject of spectrum sensing techniques has been well studied in research community, highly motivated by the emergence of cognitive radio architectures. But in practice, given the complexity of proposed spectrum sensing techniques, the implementation of cognitive radio has become a tedious task. Unlike many papers in the literature, this paper focuses on implementation aspects of spectrum sensing. Especially, we attempt to reduce the time taken for spectrum sensing based on adaptive FFT approach employing statistical analysis. The first step of the many spectrum techniques is to obtain the FFT of input samples. Most of the existing spectrum analyzers like Tektronix RSA6000, Rohde & Schwarz FSVR etc employ detection and estimations algorithms based on the FFTs of the acquired samples. In this paper, we propose an algorithm to vary the FFT-size to obtain the spectrum information at a faster rate, applicable to cognitive radio environment. FFT size would be varied in accordance with statistical information obtained from the prediction engines. The proposed Adaptive FFT algorithm is studied as applied to the well known energy detection technique. Finally an implementation is carried out on USRP based on GNU Radio platform