Research On Algorithm Of Cooperative Spectrum Sensing In Wideband Cognitive Radio Networks

The demand of spectrum is growing due to the growth of wireless business, but the fact is that the licensed spectrum resources have not been fully exploited. There is a contradiction between the allocation and the shortage of radio resource. The emergence of cognitive radio will provide effective solution to the problem of spectrum shortage, and it has been a hot topic in the research of wireless communications. Spectrum sensing is an essential task in cognitive radio. To mitigate the negative impact of frequency-selective fading on spectrum sensing, the collaborative spectrum sensing attracts the attention of researchers, but the current collaborative spectrum sensing based on the assumption that cognitive radio users keep silent during the spectrum sensing is not fit for the spectrum sensing where primary users and cognitive radio users transmit simultaneously.The mainly research in this thesis is the improvement of the current collaborative spectrum sensing algorithm to deal with the spectrum sensing where primary users and cognitive radio users transmit simultaneously. The main work in this thesis is summarized as follows:Firstly, the thesis overviews the basic conception of cognitive radio and its recent developments, and has a survey of the typical traditional spectrum sensing algorithms for cognitive radio, especially in the field of wideband cognitive radio, then analyzes the draback of the current collaborative spectrum sensing algorthm.Secondly, the thesis elaborates the theory of compresive sensing and its application, discusses the feasibility utilizing the compressive sensing to reduce the data acquisiton cost in spectrum sening.Finally, the thesis proposes an alternative algorithm of collabrotive and compressive wide-band spectrum sensing to deal with the spectrum sensing where primary users and cognitive radio users transmit simultaneously. In this algorithm, to effectively reduce the data acquisition cost, the compressive sampling technique is utilized which exploits the signal sparsity induced by network spectrum under-utilization. To collect spatial diversity against wireless fading, multiple CRs collaborate during the sensing task by enforcing consensus among local spectral estimates. To separate the estimation of spectrum occupied by primary users and CRs, an alternative method is proposed. To cope with the spectral innovations arising from interfering CRs, the orthogonality between the spectrum of primary users and that of CRs is imposed as constraints for consensus optimization during distributed collaborative sensing. The simulation results validate the vadility and convergence of the algorithm proposed in this thesis.