Blind Channel Equalization

TL;DR

This paper analyzes and compares the performance of LMS and CMA algorithms for blind channel equalization, aiming to improve data transmission quality without requiring pilot signals.

Contribution

It demonstrates the application of four CMA algorithms for blind equalization and compares their effectiveness with LMS in simulated communication channels.

Findings

CMA algorithms effectively perform blind equalization without pilot signals.

LMS and CMA show different performance characteristics in channel recovery.

Simulation results highlight the strengths and limitations of each method.

Abstract

Future services demand high data rate and quality. Thus, it is necessary to define new and robust algorithms to equalize channels and reduce noise in communications. Nowadays, new equalization algorithms are being developed to optimize the channel bandwidth and reduce noise, namely, Blind Channel Equalization. Conventional equalizations minimizing mean-square error generally require a training sequence accompanying the data sequence. In this study, the result of Least Mean Square (LMS) algorithm applied on two given communication channels is analyzed. Considering the fact that blind equalizers do not require pilot signals to recover the transmitted data, implementation of four types of Constant Modulus Algorithm (CMA) for blind equalization of the channels are shown. Finally, a comparison of the simulation results of LMS and CMA for the test channels is provided.