Blind Adaptive Reduced-Rank Detectors for DS-UWB Systems Based on Joint Iterative Optimization and the Constrained Constant Modulus Criterion

TL;DR

This paper introduces a novel blind adaptive reduced-rank receiver for DS-UWB systems that jointly optimizes a transformation matrix and a reduced-rank filter using iterative methods and the CCM criterion, enhancing interference suppression.

Contribution

It proposes a new joint iterative optimization approach for blind adaptive receivers in DS-UWB systems, with low-complexity algorithms and effective interference suppression capabilities.

Findings

Excellent performance in suppressing ISI and MAI

Low-complexity adaptive algorithms demonstrated

Effective blind channel estimation achieved

Abstract

A novel linear blind adaptive receiver based on joint iterative optimization (JIO) and the constrained constant modulus (CCM) design criterion is proposed for interference suppression in direct-sequence ultra-wideband (DS-UWB) systems. The proposed blind receiver consists of two parts, a transformation matrix that performs dimensionality reduction and a reduced-rank filter that produces the output. In the proposed receiver, the transformation matrix and the reduced-rank filter are updated jointly and iteratively to minimize the constant modulus (CM) cost function subject to a constraint. Adaptive implementations for the JIO receiver are developed by using the normalized stochastic gradient (NSG) and recursive least-squares (RLS) algorithms. In order to obtain a low-complexity scheme, the columns of the transformation matrix with the RLS algorithm are updated individually. Blind channel estimation algorithms for both versions (NSG and RLS) are implemented. Assuming the perfect timing, the JIO receiver only requires the spreading code of the desired user and the received data. Simulation results show that both versions of the proposed JIO receivers have excellent performance in suppressing the inter-symbol interference (ISI) and multiple access interference (MAI) with a low complexity.