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 down-link 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.
Bibliographical noteFunding Information:
Manuscript received November 8, 1999; revised March 12, 2001. This work was supported by the Natural Sciences and Engineering Research Council of Canada under Grant OPG0090391. The associate editor coordinating the review of this paper and approving it for publication was Prof. Dimitrios Hatzinakos.
- Decision feedback equalization
- Interior point methods
- Least-squares algorithms
- Transient analysis