Channel Estimation Analysis For TD-LTE System With Distributed MIMO

Time division long time evolution technology (TD-LTE) has been listed as the main developing direction by Chinese government for the next generation wireless communication technology, which takes the advantage of orthogonal frequency division multiple access (OFDMA) and achieves high data transmission rate. To increase the capability of the wireless channel, multiple input multiple output (MIMO) is greatly recommended by LTE standard. However, the distance among multiple antennas of one base station of traditional Honey cell can't meet the distance requirement to ensure the channels independent from each other, which lowers the capability of MIMO channel. The distributed antenna system (DAS) is a good potential replacement to solve this problem. So DAS and TD-LTE are combined here to constrcut a new wireless transmission system.The impacts of multi-path on the wideband wireless signal bring big challenge to the receiver. In this paper, based on the Jakes multi-path channel model, the time and frequency double selective fading charactoristics are analyzed and the shortage of traditional channel estimation method is also proved. In this paper, a space time and cyclic delay transmission diversity (ST-CDTD) is adopted to inhibit the frequency selective fading. Taking into account of the time selective fading, a joint channel estimation method is proposed, which combines the frequency domain minimum mean square error (MMSE) channel estimation and Kalman filter thoery, which tracks the variation of the channel in time domain and combat the estimation lag. Further, to ensure the filter could converge to the best mean value, especially when the channel varies fast in time domain, iterative estimation idea is used, which improves the estimation accuracy and lower the bit error rate (BER).To test the performance of joint estimator, the joint estimator is applied to the uplink of LTE, SC-FDMA, and the downlink, OFDMA, respectively. Here, the KF and the unscented Kalman filter (UKF) are both considered. Computer simulation results show that, within certain range of channel dynamics, the joint estimator effectively solves the estimation lag of traditonal channel estimator when the channel varies fast. With the assistance of iterative calculation, the joint estimator works steady, converges fast to the best mean value and greatly improves the BER performance of the system. Through comparison, UKF has faster convergeing speed as well as better estimation accuracy.