Channel Estimation And Signal Detection Technologies For MIMO-OFDM Communication Systems

The dispersive of wireless channel and the system bandwidth efficiency are the big challenges the future broadband wireless communication facing. Orthogonal frequency division multiplexing (OFDM) can effectively combat the multipath fading channel and convert the frequency selective channel into flat channel in frequency domain. Multiple input multiple output (MIMO) transmit multiple data streams in parrallel subchannels in spatial, thus greatly increasing the system throughput without additional bandwidth and power requirements. Combining the merits of two technologies, the bandwidth efficiency and expandability leads MIMO-OFDM to be a potential candidate for beyond 3rd generation (B3G) systems. The requirements for B3G systems such as high data rate, high quality and mobility make the transmission and receive techniques more strict. To solve the problems mentioned above, in this thesis, we focus on the design of the pilot sequences, channel estimation and signal detection techniques for MIMO-OFDM system.In order to design the pilot sequence and channel estimation algorithm, the baseband mathmatics model of MIMO-OFDM transmission in multipath fading channel is analyzed, and derived the channel estimation method and mean square error (MSE) performance based on the maximum likelihood (ML) and least square (LS). Based on these results, we derived the optimum criteria of frequency domain pilot sequences in MIMO-OFDM systems, and extend the traditional design method of orthogonal pilot sequences in two transmit antennas scenario to arbitrary transmit antennas scenario. We have also proposed a design method of frequency domain orthogonal pilot sequences which can achieve the lower bound of MSE and keep the PAPR of pilot signal to one. Finally their performance under different length of pilot sequence and number of transmit antennas are compared with traditional schemes and analyzed.The implementation and performance aspects of MIMO-OFDM system are usually depend on the complexity and performance of the receiver. We analyzed the merits and dismerits of traditional detector, and proposed a complexity reduced soft output maximum likelihood detector based on the hamming space theory. In MIMO-OFDM system, the detection can be performed on the each subcarrier, the optimum receive algorithm, maximum likelihood sequence estimation (MLSE) and maximum a posteriori (MAP) detector, are usually have much high complexity in multiple antennas and high order modulation scenario. However, the low complexity zero forcing (ZF) and minimum mean square error (MMSE) algorithm have less satisfied performance and limit its applications. In the proposed algorithm, we utilize the conventional linear detector at the initial stage to generate the log likelihood ratio of each code bits, then a hamming subspace can be spanned via the hard decision of these initial soft values. Themaximum likelihood search performed in this reduced space to generate the improved soft output of detector. For that this much smaller hamming space comared with the whole solution space has high initial reliability, the proposed algorithm can greatly reduce the complexity of maximum likelihood search and has the performance close to that of soft output MLSE.Base on the optimum receiver theory, the joint maximum likelihood receive of the whole receiver including detect, deinterleaving, decoding, etc has the best performance, while the high complexity made it can not be implemented in hardware currently and applied to the real systems. The iterative detect and decoding algorithm is a suboptimum joint receiver, can achieve the good trade off between the performance and complexity, and closing the Shannon limit performace. We analyzed the merits and dismerits of traditional iterative receiver, derived the iterative detect and decoing algorithm based on MMSE criterion, and modified the proposed complexity reduced maximum likelihood detect algorithm in 4th chapter, made it to be a low complexity soft input and soft output detect algorithm. Besides, we also modified the traditional soft input and soft output decoder's input and output on the requirements of the detector. All this constitutes a novel low complexity iterative detect and decoding algorithm. This algorithm can avoid the multiple inversion operation in the traditional iterative algorithm based on MMSE and ZF. The analysis and computer simulation also demonstrated the advantages of proposed algorithm.The close loop transmission of wireless communication usually achieves good BER performance and much higher system throughput. Based on the analysis of close loop transmission under slow varing fading channel, we investigate the effects of the power allocation, precoding, channel estimation error and channel feedback error on the system, and proposed optimized transmit preprocessing scheme for MIMO-OFDM system. Firstly, under the assumption of perfect channel estimation, perfect feedback and equal power allocation between antennas and subcarriers, we derived optimal transmit preprocessing scheme for receiver based on ML, ZF, MMSE algorithm. However, the channel estimation and feedback can not be perfect in real world, we consided this condition and derived the optimal transmit preprocessing to maximum the system capacity, and optimize the power allocation between the antennas and subcarriers according to the inner point algorithm. The proposed preprocessing take into account a variance from the channel estimation and feedback, include precoding and power allocation jointly. The proposed scheme can effectively reduce the capacity floor made from the imperfect channel estimation and feedback, and improve the BER performance. When the variance is relative small and in low SNR region, the proposed precoding with optimized power allocation can achieve higher capacity performance compared with traditional open loop transmission under perfect channel estimation and feedback.