Research On Orthogonal Space-Time Codes Blind Ml Detection Algorithm

With the rapid development of wireless communication technologies, the contradiction between the increasing data rate and the limited bandwidth is a challenge to the research on wireless communication. As an effective way to improve the spectrum utilization and channel capacity, multiple-input multiple-output (MIMO) communication technique is one of the key techniques for the next generation wireless communication system. The space-time codes which is based on the diversity introduced MIMO system can utilize spatial multiplexing and diversity to improve the performance of overall system.At the same time, it is difficult to get the exact channel estimation because of the time varying characteristics of the wireless channel environment leading to the increasing difficult of the signal detection. Therefore the blind maximum-likelihood detection with unknown channel state information (CSI) become a hot research area. This thesis deals with the problem of orthogonal space-time block codes (OSTBC) ML detection with unknown CSI.The content of this thesis are listed as follows:First this thesis describes the characteristics of wireless channel and channel model of MIMO system. Then it analyzes the capacity of MIMO system. It introduces the basic theory of the space-time coding for the MIMO system and focus on the principle of space-time block code and the maximum-likelihood decoding algorithm as well as the simulation of the different orthogonal space-time block coding. It analyze the orthogonal space-time block coding performance using the blind and the semi-blind algorithm based on the cyclic ML method. After this, it analyze the semidefinite relaxation algorithm and the sphere decoding algorithm, using this two algorithm to perform the detection of orthogonal space-time block codingFinally, a sphere decoder called IR-SD is applied to the blind ML detection of orthogonal space-time block codes. Simulation results show that performance gain can be achieved with this new method, and the IR-SD requires much lower computational complexity than other algorithms.