Blind channel equalization using variational autoencoders

Avi Caciularu, David Burshtein

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

Abstract

A new maximum likelihood estimation approach for blind channel equalization, using variational autoencoders (VAEs), is introduced. Significant and consistent improvements in the error rate of the reconstructed symbols, compared to constant modulus equalizers, are demonstrated. In fact, for the channels that were examined, the performance of the new VAE blind channel equalizer was close to the performance of a nonblind adaptive linear minimum mean square error equalizer. The new equalization method enables a significantly lower latency channel acquisition compared to the constant modulus algorithm (CMA). The VAE uses a convolutional neural network with two layers and a very small number of free parameters. Although the computational complexity of the new equalizer is higher compared to CMA, it is still reasonable, and the number of free parameters to estimate is small.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Communications Workshops, ICC Workshops 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
ISBN (Electronic)9781538643280
DOIs
StatePublished - 3 Jul 2018
Event2018 IEEE International Conference on Communications Workshops, ICC Workshops 2018 - Kansas City, United States
Duration: 20 May 201824 May 2018

Publication series

Name2018 IEEE International Conference on Communications Workshops, ICC Workshops 2018 - Proceedings

Conference

Conference2018 IEEE International Conference on Communications Workshops, ICC Workshops 2018
Country/TerritoryUnited States
CityKansas City
Period20/05/1824/05/18

Funding

FundersFunder number
Israel Science Foundation1082/13

    Keywords

    • Blind channel equalization
    • Constant modulus algorithm
    • Convolutional neural networks
    • Deep learning
    • Maximum likelihood
    • Variational autoencoders

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