Adaptive Low-rank Constrained Constant Modulus Beamforming Algorithms using Joint Iterative Optimization of Parameters

TL;DR

This paper introduces a robust reduced-rank adaptive beamforming scheme based on joint iterative optimization, improving performance and efficiency in handling large filter arrays for signal estimation.

Contribution

It presents a novel joint iterative optimization approach for low-rank constrained constant modulus beamforming, with new algorithms demonstrating superior performance.

Findings

Superior beamforming performance in simulations

Effective handling of large filter arrays

Low-complexity adaptive algorithms

Abstract

This paper proposes a robust reduced-rank scheme for adaptive beamforming based on joint iterative optimization (JIO) of adaptive filters. The scheme provides an efficient way to deal with filters with large number of elements. It consists of a bank of full-rank adaptive filters that forms a transformation matrix and an adaptive reduced-rank filter that operates at the output of the bank of filters. The transformation matrix projects the received vector onto a low-dimension vector, which is processed by the reduced-rank filter to estimate the desired signal. The expressions of the transformation matrix and the reduced-rank weight vector are derived according to the constrained constant modulus (CCM) criterion. Two novel low-complexity adaptive algorithms are devised for the implementation of the proposed scheme with respect to different constrained conditions. Simulations are performed to show superior performance of the proposed algorithms in comparison with the existing methods.