Robust spectrum sensing for mobile cognitive radio

Abstract:

Cognitive radio (CR) is the solution to the spectrum scarcity issue faced in wireless communications. It allows unlicensed secondary users (SUs) to opportunistically access the spectrum assigned to licensed primary users (PUs), but unused, on a non-interfering basis. Spectrum sensing is the pro- cess by which the CR node becomes aware of the spectrum occupancy, in order to decide which frequency bands to use. It is therefore a critical step in CR. The main goal of this thesis is to design robust spectrum sens- ing algorithms for mobile cognitive radio systems. The contributions are grouped in three parts. In the first part, a new spectrum sensing algorithm is proposed for mo- bile CR environments. It exploits the knowledge of the SU’s mobility pa- rameters, by using the Bayesian changepoint detection theory. Moreover, it works for practical scenarios where the PU’s signal power is unknown to the SU. Simulation results show that the derived algorithm is a good choice when the signal-to-noise ratio (SNR) is unknown and the SU could be required to detect very low SNR signals. In the second part, the issue of joint transmission and sensing is stud- ied in mobile CR. Considering a scenario with multiple PU appearances and disappearances, a new framework that uses changepoint detection for spectrum sensing is introduced. The optimal system parameters (sensing time, transmission time and detection threshold) that maximize the spec- trum utilization are numerically computed and analysed. In the last part, it is considered that some information about the existing PUs is available in a geolocation database. Then, a new spectrum sensing algorithm, that exploits this geolocation information by using the Bayesian.

DOI:

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