SAGE Algorithm for Channel Estimation and Data Detection Using Superimposed Training in MIMO System

摘要

Recently, the superimposed pilot channel estimation has attracted attention for wireless communications, where the pilot symbol sequence is superimposed on a data symbol sequence and transmitted together, and thus there is no drop in information rate. In the superimposed pilot channel estimation, the receiver correlates the received symbol sequence with the pilot symbol sequence, and obtains the channel estimate. However, the correlation between the pilot symbol sequence and the data symbol sequence deteriorates the channel estimation accuracy. In particular, the channel estimation accuracy of the superimposed pilot channel estimation scheme is significantly deteriorated in MEMO systems, because the pilot symbol power of each transmit antenna to the total transmit power of all transmit antennas becomes smaller as the number of transmit antennas increases. On the other hand, it has been well known that the SAGE algorithm is an effective method for channel estimation and data detection. This algorithm is particularly effective in MEMO systems, because the operation of this algorithm can cancel the interference from other transmit antennas. In this paper, we propose a SAGE algorithm for channel estimation and data detection using superimposed pilot channel estimation in MEMO systems. From the results of computer simulations, we show that the proposed system can achieve the good BER performances by using the SAGE algorithm with iteration.