Training Symbol-Based Equalization for Quadrature Duobinary PDM-FTN Systems

TL;DR

This paper introduces a training symbol-based equalization algorithm for polarization de-multiplexing in quadrature duobinary FTN systems, improving convergence and reducing required optical signal-to-noise ratio for reliable communication.

Contribution

It presents a novel LMS-based equalization method considering multiple symbol locations, enhancing polarization de-multiplexing in QDB-modulated FTN systems.

Findings

Reduces optical SNR requirement by 1.7-1.8 dB for target BER

Achieves excellent convergence performance under various polarization scenarios

QDB with 4-QAM is preferable over Tomlinson-Harashima precoding in FTN systems

Abstract

A training symbol-based equalization algorithm is proposed for polarization de-multiplexing in quadrature duobinary (QDB) modulated polarization division multiplexedfaster-than-Nyquist (FTN) coherent optical systems. The proposed algorithm is based on the least mean square algorithm, and multiple location candidates of a symbol are considered in order to make use of the training symbols with QDB modulation.Results show that an excellent convergence performance is obtained using the proposed algorithm under different polarization alignment scenarios. The optical signal-to-noise ratio required to attain a bit error rate of 2*10-2 is reduced by 1.7 and 1.8 dB using the proposed algorithm, compared to systems using the constant modulus algorithm with differential coding for 4-ary quadrature amplitude modulation(4-QAM) and 16-QAM systems with symbol-by-symbol detection, respectively.Furthermore, comparisons with the Tomlinson-Harashima precoding-based FTN systems illustrate that QDB is preferable when 4-QAM is utilized.