Interior point least squares estimation: transient convergence analysis and application to MMSE decision-feedback equalization
In many communication systems, training sequences are used to help the receiver identify and/or equalize the channel. The amount of training data required depends on the convergence properties of the adaptive filtering algorithms used for equalization. In this paper, we propose the use of a new adaptive filtering method called interior point least squares (IPLS) for adaptive equalization. First, we show that IPLS converges exponentially fast in the transient phase. Then, we use the IPLS algorithm to update the weight vector for a minimum-mean-square-error decision-feedback equalizer (MMSE-DFE) in a CDMA downlink scenario. Numerical simulations show that when training sequences are short IPLS consistently outperforms RLS in terms of system bit-error-rate and packet error rate. As the training sequence gets longer IPLS matches the performance of the RLS algorithm.