Research On Detection Techniques In MIMO System

Multiple input multiple output (MIMO) system provides high spectrum efficiency through adopting multiple antennas in the transmitter and receiver. Therefore, MIMO communication techniques can meet the requirements of high throughputs in the future wireless communication system and have been paid more and more attentions. Signal detection is one of the most important techniques in the receiver of MIMO system. In order to improve the performance of MIMO detection over Rician channels, the thesis combines MIMO detection methods with space time coding and channel decoding techniques together and optimizes them. The main contents of the thesis are as follows:Firstly, the characteristics of wave propagation over wireless channel, including the fading of large scale and small scale, are introduced. The advantages and disadvantages of space time coding widely used are compared. Besides, the capacity of the rician channel in MIMO system is simulated and analyzed.Secondly, the existing algorithms of MIMO detection are sorted, and the basic principles of some classical algorithms are introduced, besides, the advantages and disadvantages of them are shown. Then, the performance of the basic algorithms is simulated over Rician channel. Simulation results indicate that the near-maximum likelihood (ML) algorithm is with low complexity and approximate performance of ML method. So it is more suitable to practical applications.Thirdly, the thesis introduces a multiple-space-time-diversity (M-STTD) system based on combined space-time codes. It proposes a near-ML algorithm for M-STTD system based on interference cancellation between antenna units. The candidate sequences for each antenna are established on the basis of the proposed algorithm at first. Then, the receiver iteratively searches for an optimal sequence one by one through examining the candidate sequences until the early termination condition is satisfied. The simulation results manifest that the proposed detecting method can achieve the near-ML performance with significantly reduced complexity versus conventional near-optimal algorithm.Finally, to improve the system performance on MIMO detection under severe fading conditions, the algorithm of iterative tree search detection, which combines soft-detection and channel decoding, is investigated. An algorithm of iterative tree search detection based on equivalent model of concatenated modules is presented to overcome the disadvantages of conventional methods where the parameters selections restrict each other. In the algorithm proposed, the process of parameters selections is equivalent to model of three concatenated modules, and every module is independent each other. The simulation results manifest that the proposed detecting method can provide a more flexible tradeoff between system performance and the complexity of MIMO detection.