Channel Estimation And Interference Cancellation For Multi-Antenna Systems

Multiple input multiple output (MIMO) technique, which provides remarkably increase in data rate and transmission quality, has become one of the key schemes in future wireless communication systems. In MIMO mobile radio environment, the presence of multi-antenna interference (MAI) and channel-introduced inter-symbol interference (ISI) both impact the bit error rate in receiver. Hence, accurate channel estimation, which is the basic of equalization and decoding, as well as interference cancellation simultaneously influence the system performance. In this paper, several channel estimation and interference cancellation algorithms are discussed in point-to-point frequency selective MIMO channels, simulation results are also presented.Superimposed training-based channel estimation algorithms arithmetically add the training sequences to the information data, no extra time is occupied, so there is no loss in spectral efficiency. These algorithms have low computational complexity because the channel state information can be achieved only based on first-order statistics of receiving symbols. Channel estimation using implicit training (IT) in single input single output (SISO) systems is introduced in this paper. Then the author generalizes this method to MIMO case. By properly assigning the IT to different transmit antennas, different sub-channels are easily separated and estimated. In addition, the proposed algorithm keeps the low peak-to-average symbol power ration as IT method does.Considering the MAI and ISI in MIMO systems, linear minimum mean-square error (MMSE) algorithm and nonlinear iterative probabilistic data association (PDA) algorithm are discussed in this paper. Then the author proposes a modified PDA algorithm combined with successive interference cancellation (SIC), which utilizes the QR decomposition of the channel matrix. The proposed method eliminates MAI perfectly, and converts matrix operation of traditional PDA equalization into numerical computation, therefore an optimum performance with reduced complexity is obtained.