Studies On Channel Estimation And Signal Detection In MIMO Systems

By adopting multiple antennas in both transmitter and receiver, the capacity and spectrum efficiency of wireless communication systems can be increased significantly without the expense of bandwidth. In MIMO (Multiple-Input Multiple-Output) systems, with the increasing number of channel parameters and the extension of transmit signals in space dimension, the channel estimation and the signal detection techniques often become the bottleneck of system performance and practical application. In this dissertation, the channel estimation and the signal detection in MIMO systems are studied respectively, which mainly includes optimal design of pilot sequence and algorithms of channel estimation and signal detection in MIMO iterative receiver.In the pilot design, two distinct pilot structure, superimposed structure and Time Division Multiplex structure are studied. For the superimposed structure, based on the first-order statistic channel estimation algorithm, a jointly-optimized pilot scheme in frequency domain is proposed, which provides the optimized pilot sequence with the best channel estimation performance, PAPR (peak-to-average power ratio) and the effective SNR(signal-to-noise ratio). By applying the idea of designing pilot in frequency domain, another robust superimposed pilot scheme is proposed to eliminate the interference of dc-offset, which avoid the interference of dc-offset on channel estimation, and thus greatly reduced the complexity introduced to estimate the dc-offset. For the TDM structure, to solve the high PAPR problem of QPP-α(quasi-periodic placement) scheme, introduced by zeros symbols in the pilot sequence, a suboptimal CM (Constant-Modular) pilot design scheme is proposed, which can effectively drop the system PAPR at the little loss of capacity.Using the CM pilot sequence to obtain the initial channel estimation, the EM (Expectation-Maximization) channel estimation algorithm and the APP (A Posteriori Probability) detection algorithm in MIMO iterative receiver are studied under the different transmission environment. By improving the deficiency of traditional algorithms, better trade-off between algorithm performance and implementation complexity are achieved. The detail is depicted as the following:In flat fading environment, to further reduce the redundancy of the existing non-exhaustive list APP detection algorithms, which is induced by setting a fixed and large list size, an adaptive size list sphere decoding (ASLSD) algorithm is proposed. The proposed algorithm makes the length of detection list varies adaptively with the SNR and the iteration. At the cost of slight loss on performance, the detection list is much shortened, and the complexity is significantly reduced.In flat fast fading environment, EM-KALMAN smooth algorithm neglects the interaction between different function modules of iterative receiver, which degrades the performance. According to the factor graph and the sum product algorithm, a EM-KALMAN prediction algorithm is proposed, which ensures the consistence of the channel estimation algorithm and the signal detection algorithm, and achieves better performance.The MCMC (Markov Chain Monte Carlo) detection algorithm can gain 2dB on performance with the complexity still less than the LSD algorithm, but in the case of high SNR or iteration, it is likely to "trap" in a certain sample state, which leads to a biased APP estimation. A Forced-Dispersed algorithm is proposed to solve this problem. In the proposed algorithm, by randomly dispersing the trapped sample sequence in a certain range, the dependence of sample sequence on APP is lowered, and the dispersed sample sequence may still have relative large APP. Compared with the other methods aiming at the "trap" problem, the proposed algorithm can increase the number of sample state dramatically, and thus achieve better detection performance.In multipath environment, the decomposed EM channel estimation algorithm has the advantage of low complexity, but the weight factor is set to be an average value without considering its effect on the channel response update, which lowers the performance. Based on the MMSE (Minimum Mean Square Error) criterion of channel estimation, an optimal weight factor setting scheme is proposed, which effectively improve the performance of decomposed EM channel estimation algorithm.In Turbo MMSE equalization algorithm, the interference is restrained by interference canceling, which deteriorates the performance due to the residual interference. To improve the performance, a space-time separated adaptive equalization algorithm is proposed. By extracting the space signal, the equalization under multipath environment is transformed to the APP detection under flat fading environment. During the detection, the PDA (Probabilistic Data Association) algorithm and the grouped MAP (Maximum a posteriori probability) algorithm can be selected adaptively according to the interference extent. The proposed adaptive equalization algorithm outperforms the Turbo MMSE equalization algorithm remarkably, moreover by adjusting the group length and the interference threshold, the algorithm can obtain a flexible trade-off between performance and complexity.