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 rece iver identify and/or equalize the channel, The amount of training data requ ired depends on the convergence properties of the adaptive filtering algori thms used for equalization. In this paper, we propose the use of a new adap tive filtering method called interior point least squares (IPLS) for adapti ve equalization. First, we show that IPLS converges exponentially fast in t he transient phase. Then, we use the IPLS algorithm to update the weight ve ctor 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.