Research On Broadband Spectrum Compression Sensing Technology Based On Clustering

From seventy or eighty’s of the last century to now, communication technology and speedhave a very big development. Both communication speed, and network technology have greatly improved. But due to the spectrum resources limited, it makes the increasing demand with limited spectrum resources has become a sharp contradiction.Spectrum utilization rate is not high, for the development of cognitive radio technology it also has made great influence, become an obstacle restricting the development of radio technology. The emergence of cognitive radio technology will solve the problem of the shortage of spectrum, it can dynamically detect authorized user spectrum have been occupied, whether the spectrum hole, namely the spectrum perception. So that the cognitive user access, and other authorized users can access when out, cognitive users can find new spectrum holes at the same time, this can improve the utilization of spectrum. Spectrum sensing in cognitive radio and dynamic spectrum allocation and spectrum management three big technology. The spectrum sensing is the key to the cognitive radio technology, it is the precondition of spectrum allocation and spectrum management. Our main research topic is how to improve the detection probability of authorized users spectrum Pa, at the same time reduce the empty rate Pf. Because aware of every single user spectrum main user is limited, so we are based on compression perception of broadband spectrum technology is introduced. With the introduction of technology based on clustering of awareness. On the premise of guarantee the detection probability, as far as possible to reduce false alarm probability, so as to improve spectrum efficiency. Chapter five:The first chapter is about the background and significance of cognitive radio technologyhas been introduced. Research and development status of cognitive radio function, including the contents, the domestic and foreign second and meaning of this thesis is introduced. Finally, the structure and the arrangement of the content of this paperintroduces the.The second chapter, basic knowledge of spectrum sensing technology provides an overview and technical classification, discuss a single spectrum sensing technology and performance parameters of the basic model, the energy detection method based onThe transmitter, the matched filter detection and cyclostationary feature detection The methods are the focus of research, comparative analysis and discussion of the advantages and disadvantages of local spectrum sensing technology and cooperative spectrum sensing technology, and finally introduced fusion rules relating AND and OR integration rules.The third chapter of the compressed sensing technique for a simple overview, which includes compressed sensing technology mainly three aspects (signal sparse representation, constructed and reconstructed signal measurement matrix) is an important step in the introduction were focused, then describes some applications related to compressed sensing techniques in cognitive radio spectrum sensing. Secondly, the "local compressive sensing" (Local Compressive Sensing, LCS) and the "centralized compressed sensing" (Centralized Compressive Sensing, CCS) algorithm is described, while the local LCS compressed sensing algorithm and centralized compressed sensing algorithm CCS Their implementation steps were introduced.The fourth chapter speaks of fuzzy clustering and its first steps were described. Second, in the "local compressive sensing" (Local Compressive Sensing, LCS) and the "centralized compressed sensing" (Centralized Compressive Sensing, CCS) proposed cluster-based local compressive sensing algorithm (Clustering-based Local Compressive Sensing algorithm based on C-LCS) and the "cluster-based joint compressive sensing" (Clustering-based Centralized Compressive Sensing, C-CCS) algorithm and then these two algorithms are described. While focusing on the steps of C-LCS and C-CCS algorithm implementations were introduced, while comparing the advantages and disadvantages of the four algorithms and conducted in-depth analysis. Finally, MATLAB simulation software and compression ratio in different SNR conditions were simulated for four algorithms for the simulation results obtained are summarized and given to do the analysis.First chapter of the work summarized in this paper, it followed by the work done on the next prospect.