Improving the Sage Algorithm with Adaptive Partial Interference Cancellation

The performance of the SAGE algorithm in channel parameter estimation could be severely degraded by the error accumulated from the interference cancellation (IC) stage as each path in the channel is estimated sequentially. This is a direct result of removing inaccurate estimate of the interfering paths, and errors occurring at the IC stage for the first few paths will be accumulated and influence the process of the paths later on. Adaptive interference cancellation suggested by Xue [1] employs a set of weights to control the the amount of interferences removed from each interferer. We extend this idea to the SAGE algorithm, where the weights are obtained by minimising the mean square error between the received signal and its estimate through a least mean square (LMS) algorithm. Simulation results show the proposed scheme can outperform the standard SAGE algorithm in non-LOS environments.