Steepest Descent Multimodulus Algorithm for Blind Signal Retrieval in QAM Systems

TL;DR

This paper introduces a steepest descent implementation of the multimodulus algorithm for blind signal retrieval in QAM systems, offering smoother convergence and better steady-state performance than traditional stochastic gradient methods.

Contribution

The paper presents a novel steepest descent approach for MMA2-2, improving convergence and steady-state performance in blind signal retrieval for digital communications.

Findings

Steepest descent MMA2-2 outperforms stochastic gradient in convergence speed.

The proposed method reduces inter-symbol interference effectively.

It achieves superior steady-state performance in QAM signal retrieval.

Abstract

We present steepest descent (SD) implementation of multimodulus algorithm (MMA2-2) for blind signal retrieval in digital communication systems. In comparison to stochastic approximate (gradient descent) realization, the proposed SD implementation of MMA2-2 equalizer mitigates inter-symbol interference with relatively smooth convergence and superior steady-state performance.