Research On Techniques For Channel Estimate And Signal Detection For Multiple Antenna System

In this dissertation, we research the problem of channel estimation and signal detection in wireless multiple-input-multiple-output (MIMO) system. The applications of MIMO technique include space diversity (such as space-time coding) or space multiplex (such as V-BLAST). We have studied two systems in this dissertation, and improved the existing algorithms and compared with them. Iterative processing can efficiently improve the performance of algorithm, and in this dissertation, we concern with the iterative algorithm and the joint estimation of channel and signal under different MIMO systems. Further more, we also studied the problem of blind estimation and equalization of MIMO systems without training symbols. The main contents in this dissertation include:1,Sequential interference cancellation (SIC) algorithm for V-BLAST, including zero-forcing or MMSE based SIC and QR decomposition (QRD) based method, are studied. We have given the system model and description and analysis of these algorithms. To tackle the problem of error propagation, we propose an improved QRD based method which combines SIC and parallel interference cancellation (PIC). Advantages of the new algorithm have been verified by the simulations and complexity analysis.2,A prior probability (APP) MIMO detection algorithms with error correct coding (ECC) are studied. The two problems of such detection are, how to explore the soft information from the decoder, and how to find the optimal sequence to achieve APP detection by efficient searching. In the dissertation, we show how to implement the Turbo iteration by exchanging soft information between detector and decoder in the soft-in-soft-out (SISO) receiver. To achieve high efficient searching algorithm for APP MIMO detector, some of the prevalent algorithm are given, which include the list sphere decoding (LSD), the list sequential algorithm (LISS) and the M algorithm. In addition, we have proposed an improved LISS algorithm and an method applying soft-Viterbi M algorithm (SOMA) to MIMO detection. The improved LISS algorithm performs stack searching with the length bias, but avoids the time-consuming auxiliary stack operation in the original LISS algorithm. Both of the improved LISS algorithm and the SOMA for MIMO detection are verified via the simulations.3,The minimum mean square error (MMSE) based MIMO detector is also studied in this dissertation. At first, we still present how to explore soft information from decoder in MMSE based MIMO detector, which is different from the cases of the APP detector. In the relevant chapter, we have given and analyzed some MMSE based MIMO detecting techniques, such as linear MMSE (LMMSE) detector, nonlinear MMSE (NMMSE) detector, PIC/SIC methods and soft IC and hard IC algorithms. A new group-GPDA algorithm based on the NMMSE detection is proposed. Different from the original GPDA algorithm, new algorithm groups the signal to be detected and alternately perform MAP detection in each group, at the meantime, approximate the other groups as Gaussian noise with matched mean and matched variance. Such processing reduces the interference components and improves the performance by applying MAP detector to the group under detecting. By adjust the size of group, tradeoff between performance and complexity can be achieved. The performance improvement has been verified via the simulations.4,Another important MIMO technique is space-time coding (STC). The performance of STC decoding is greatly affected by channel estimate error, which is hard to avoid in practice due to limit numbers of pilots in OFDM symbol or time varying channel. In the chapter, an iterative joint channel estimate and space-time block coding (STBC) algorithm is proposed, based on the analysis of existing iterative algorithm, such as the EM algorithm and the cyclic minimization algorithm. The new algorithm greatly improves the performance of STBC decoding and channel estimate but avoid the time-consuming searching operations in those existing STBC decoding methods. In addition, the time-domain channel estimate avoids the limitation on constant-modular signals of existing method, which is to reduce the complexity of channel estimate, by efficiently using the FFT and IFFT to convert the channel parameters between time domain and frequency domain. In the simulations, our method has been verified to be approaching the curve with ideal channel state information (CSI), in the case of OFDM system with evenly inserted pilots, and the case of time-varying channel.5,Blind channel estimate and equalization for MIMO system is a problem to be studied in this dissertation. In the relevant chapter, we proposed a neat derivation for subspace based method for MIMO system. With this method, the problems of channel estimate and equalization with colored signal input have been studied. There exists an ambiguous matrix after the step of MIMO blind equalization. That is a problem of instantaneous mixture to be resolved. In our method, we explore the joint diagonalization to resolve it and the satisfied results have been gained.