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Approximate ML Serial Detector Based on Tomlinson-Harashima Pre-Equalization

Authors: Lingyang Song; Rodrigo C. de Lamare; Are Hjørungnes; Manav R. Bhatnagar; Alister G. Burr;

Approximate ML Serial Detector Based on Tomlinson-Harashima Pre-Equalization

Abstract

In this paper, we propose a novel and simple approximate maximum likelihood detector (A-ML-D) for single input and single output (SISO) systems over frequency-selective fading channels based on Tomlinson-Harashima pre-equalizer. By assuming full channel state information (CSI) at the transmitter side, the pre-equalizer can remove some inter-symbol interference (ISI) at the transmitter. At the receiver, we implement a Gaussian approximation, a pre-whitening filter and a matched filter to realize a set of parallel SISO schemes, and thus, facilitate single symbol detection. The proposed scheme can obtain full multi-path diversity and achieve near-optimal performance with a complexity lower than linear MMSE and MMSE-DFE. Analytical symbol error rate (SER) is derived to further justify the proposed detector.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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