TY - GEN
T1 - Blind channel equalization using variational autoencoders
AU - Caciularu, Avi
AU - Burshtein, David
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/3
Y1 - 2018/7/3
N2 - 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.
AB - 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.
KW - Blind channel equalization
KW - Constant modulus algorithm
KW - Convolutional neural networks
KW - Deep learning
KW - Maximum likelihood
KW - Variational autoencoders
UR - http://www.scopus.com/inward/record.url?scp=85050284755&partnerID=8YFLogxK
U2 - 10.1109/ICCW.2018.8403666
DO - 10.1109/ICCW.2018.8403666
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AN - SCOPUS:85050284755
T3 - 2018 IEEE International Conference on Communications Workshops, ICC Workshops 2018 - Proceedings
SP - 1
EP - 6
BT - 2018 IEEE International Conference on Communications Workshops, ICC Workshops 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Communications Workshops, ICC Workshops 2018
Y2 - 20 May 2018 through 24 May 2018
ER -