Signal Processing Technology Of60Ghz Communication System With Compressed Sensing

With the rapid development of wireless communication technology which leads to the Gbps era,60Ghz millimeter-wave wireless communication technology becomes the hot topic due to its advantages like unlicensed wide bandwidth and strong security. However, the transmission rate as high as Gbps raises new requirements on punctured convolutional code and signals’sampling and other relevant areas.On one way, the punctured convolutional codes used in60Ghz technology were analyzed, and this paper turn the search of good punctured convolutional codes into a mathematical optimization problem and used simulated annealing algorithm to search out good punctured convolutional codes to improve the system reliability and reduce the error rate in60Ghz techonology.On the other way, the philosophies of60Ghz technology and compressed sensing technology were analyzed, and this paper focused on the feasible combinations between relevant areas, and then deeply researched into the following questions:Firstly, philosophies of blind source separation were analyzed, and then this paper proposed a three-step blind source separation algorithm which firstly estimated the mixing matrix with AK-means algorithm and then used KSVD to get sparse expression base with better performances, finally took Bayesian compressed sensing algorithm to separate out original signals.Secondly, philosophies of signal recognition were analyzed, and then this paper proposed a method which trained sparse expression matrix and used Bayesian compressed sensing algorithm to get signal’s useful characteristics, thus judged out the signal’s relevant information with the help of pattern classification algorithm. All the above methods’good performances had been proved through simulations in Matlab platform.