USRP Model Simulation And Testing Based On Energy Spectrum Sensing

As an emerging novel wireless communication technology, cognitive radio, with a potential to ease the current strain on radio frequency resources, has become a research hotspot in the area of communication technology. This dissertation presents a study of spectrum sensing, which is one of the key technologies of cognitive radio, from a perspective of prevalent spectrum sensing technologies.First, based on Monte Carlo, models are built for energy detection, loop feature detection, singular value decomposition and maximum eigenvalues detection. The performance of these models is evaluated through simulation. The simulation verified that energy detection in comparison with the other three detection methods has a better performance under the same circumstances. After a calculation of the theoretical curve of energy detection and a simulation of noise uncertainty, a conclusion could be made that the simulation result agrees with energy detection theory and the performance of energy detection worthens as noise level rises.Secondly, as powerful with simulation as matlab is, it is deployed in building a dynamic detection and simulation platform and this spectrum detection technology is presented to users with a graphical interface. The platform introduces energy detection and singular value decomposition. Upon the consideration that in decayed channels the performance of single user detection deteriorates dramatically, square-law combination and square-law selection are analised and implemented. Also the problem that the efficiency of curve detection will be at a low level when Monte Carlo method is used singularly, this platform creates simulated dynamic spectrum for both the signal in question and noise signals.For how to achieve real-time air signal spectrum sensing, software radio equipment USRP real-time signal detection system developed at the physical layer, which can effectively detect the period of the signal spectrum. Provide a research platform for the future cognitive radio. Finally, although there has been a significant improvement in spectrum detection theory alongside the emergence of cognitive radio, but due to the complexity of implementation and restraints in available hardware, cognitive radio is still confined to theory. As for spectrum sensing of aerial signals, this dissertation develops a real-time signal detection system in a physical sense with the help of wireless device USRP. The platform can effectively detect signals in a specific frequency range. This dissertation platform provides a building ground for future cognitive radio platforms.