16-QAM probabilistic constellation shaping by learning the distribution of transmitted symbols from the training sequence

A. Fallahpour; F. Alishahi; A. Minoofar; K. Zou; A. Almaiman; P. Liao; H. Zhou; M. Tur; A. E. Willner

16-QAM probabilistic constellation shaping by learning the distribution of transmitted symbols from the training sequence

A. Fallahpour^*, F. Alishahi, A. Minoofar, K. Zou, A. Almaiman, P. Liao, H. Zhou, M. Tur, A. E. Willner

^*Corresponding author for this work

School of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

A technique for probabilistic constellation shaping based on distribution learning from a training sequence is investigated. In this approach, the probability distribution is optimized such that it can maximize the mutual information. The effectiveness of this approach is verified by shaping 10 Gbaud 16QAM in simulation and experiment.

Original language	English
Title of host publication	Optical Fiber Communication Conference, OFC 2020
Publisher	OSA - The Optical Society
ISBN (Print)	9781943580712
State	Published - 2020
Event	Optical Fiber Communication Conference, OFC 2020 - San Diego, United States Duration: 8 Mar 2017 → 12 Mar 2017

Publication series

Name	Optics InfoBase Conference Papers
Volume	Part F174-OFC 2020

Conference

Conference	Optical Fiber Communication Conference, OFC 2020
Country/Territory	United States
City	San Diego
Period	8/03/17 → 12/03/17

Cite this

Fallahpour, A., Alishahi, F., Minoofar, A., Zou, K., Almaiman, A., Liao, P., Zhou, H., Tur, M., & Willner, A. E. (2020). 16-QAM probabilistic constellation shaping by learning the distribution of transmitted symbols from the training sequence. In Optical Fiber Communication Conference, OFC 2020 (Optics InfoBase Conference Papers; Vol. Part F174-OFC 2020). OSA - The Optical Society.

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title = "16-QAM probabilistic constellation shaping by learning the distribution of transmitted symbols from the training sequence",

abstract = "A technique for probabilistic constellation shaping based on distribution learning from a training sequence is investigated. In this approach, the probability distribution is optimized such that it can maximize the mutual information. The effectiveness of this approach is verified by shaping 10 Gbaud 16QAM in simulation and experiment.",

author = "A. Fallahpour and F. Alishahi and A. Minoofar and K. Zou and A. Almaiman and P. Liao and H. Zhou and M. Tur and Willner, {A. E.}",

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year = "2020",

language = "אנגלית",

isbn = "9781943580712",

series = "Optics InfoBase Conference Papers",

publisher = "OSA - The Optical Society",

booktitle = "Optical Fiber Communication Conference, OFC 2020",

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Fallahpour, A, Alishahi, F, Minoofar, A, Zou, K, Almaiman, A, Liao, P, Zhou, H, Tur, M & Willner, AE 2020, 16-QAM probabilistic constellation shaping by learning the distribution of transmitted symbols from the training sequence. in Optical Fiber Communication Conference, OFC 2020. Optics InfoBase Conference Papers, vol. Part F174-OFC 2020, OSA - The Optical Society, Optical Fiber Communication Conference, OFC 2020, San Diego, United States, 8/03/17.

16-QAM probabilistic constellation shaping by learning the distribution of transmitted symbols from the training sequence. / Fallahpour, A.; Alishahi, F.; Minoofar, A. et al.

Optical Fiber Communication Conference, OFC 2020. OSA - The Optical Society, 2020. (Optics InfoBase Conference Papers; Vol. Part F174-OFC 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Fallahpour, A.

AU - Alishahi, F.

AU - Minoofar, A.

AU - Zou, K.

AU - Almaiman, A.

AU - Liao, P.

AU - Zhou, H.

AU - Tur, M.

AU - Willner, A. E.

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AB - A technique for probabilistic constellation shaping based on distribution learning from a training sequence is investigated. In this approach, the probability distribution is optimized such that it can maximize the mutual information. The effectiveness of this approach is verified by shaping 10 Gbaud 16QAM in simulation and experiment.

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PB - OSA - The Optical Society

Y2 - 8 March 2017 through 12 March 2017

ER -

16-QAM probabilistic constellation shaping by learning the distribution of transmitted symbols from the training sequence

Abstract

Publication series

Conference

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