Blind Adaptive Interference Suppression Based on Set-Membership Constrained Constant-Modulus Algorithms with Time-Varying Bounds

TL;DR

This paper introduces novel blind adaptive interference suppression algorithms based on set-membership constrained constant modulus techniques with dynamic bounds, improving performance in multiuser communication systems.

Contribution

It develops new stochastic gradient and RLS algorithms incorporating dynamic bounds and a comprehensive framework for channel and amplitude estimation in multiuser scenarios.

Findings

Outperform existing techniques in DS-CDMA simulations

Require fewer parameter updates

Reduce overbounding and underbounding risks

Abstract

This work presents blind constrained constant modulus (CCM) adaptive algorithms based on the set-membership filtering (SMF) concept and incorporates dynamic bounds {for interference suppression} applications. We develop stochastic gradient and recursive least squares type algorithms based on the CCM design criterion in accordance with the specifications of the SMF concept. We also propose a blind framework that includes channel and amplitude estimators that take into account parameter estimation dependency, multiple access interference (MAI) and inter-symbol interference (ISI) to address the important issue of bound specification in multiuser communications. A convergence and tracking analysis of the proposed algorithms is carried out along with the development of analytical expressions to predict their performance. Simulations for a number of scenarios of interest with a DS-CDMA system show that the proposed algorithms outperform previously reported techniques with a smaller number of parameter updates and a reduced risk of overbounding or underbounding.