Digital-Analog Hybrid Equalization of Broadband Signals Based on Equivalent Time Sampling

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

As the symbol rate gradually increases, inter-symbol interference becomes more serious. High-performance and low-resource equalization technology has become one of the research hotspots in the signal processing area. In this paper, for channel with constant characteristics, a digital-analog hybrid equalizer of broadband signals based on equivalent time sampling is proposed. The equivalent time sampling technology can reconstruct signals at a sampling rate much lower than the Nyquist sampling rate, thus, it can greatly alleviate the pressure on the ADC and reduce the processor's resource consumption. In addition, the dual-mode blind equalization algorithm can achieve fast convergence and accurately compensate for errors caused by non-ideal channel. Moreover, the numerical results show that the performance of the dual-mode multi-modulus algorithm and decision-directed algorithm (MMA-DD) is better than the multi-modulus algorithm (MMA) which can further reduce the residual error to accurately track the channel characteristics.

Original languageEnglish
Title of host publication2020 International Wireless Communications and Mobile Computing, IWCMC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages337-340
Number of pages4
ISBN (Electronic)9781728131290
DOIs
Publication statusPublished - Jun 2020
Event16th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2020 - Limassol, Cyprus
Duration: 15 Jun 202019 Jun 2020

Publication series

Name2020 International Wireless Communications and Mobile Computing, IWCMC 2020

Conference

Conference16th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2020
Country/TerritoryCyprus
CityLimassol
Period15/06/2019/06/20

Keywords

  • Channel equalization
  • equivalent time sample
  • signal reconstruction

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