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Communications Using Deep Learning T...

Pachpande, Priti G.

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Communications Using Deep Learning Techniques.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Communications Using Deep Learning Techniques./
Author:	Pachpande, Priti G.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	51 p.
Notes:	Source: Masters Abstracts International, Volume: 80-11.
Contained By:	Masters Abstracts International80-11.
Subject:	Computer Engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13878783
ISBN:	9781392129456

Communications Using Deep Learning Techniques.
Pachpande, Priti G.

Communications Using Deep Learning Techniques. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 51 p.

Source: Masters Abstracts International, Volume: 80-11.

Thesis (M.S.)--State University of New York at Albany, 2019.

This item must not be sold to any third party vendors.

Deep learning (DL) techniques have the potential of making communication systems more efficient and solving many problems in the physical layer. In this thesis, we proposed an end-to-end communication system for single user and multi-user using a neural network (NN) architecture known as autoencoder (AE). At first, we propose a system which can be deployed in Visible Light Communication (VLC) system where the system is tested in different scenarios using various AE parameters and applied in an indoor VLC model. Symbol error rate (SER) is evaluated with respect to the signal-to-noise ratio (SNR) values at different locations within the room. Then, we extend the work to include multi-user transmission. Additionally, an alternative architecture is introduced for a multi-carrier modulation scheme for radio frequency communication. It is evaluated under additive white Gaussian noise (AWGN) and Rayleigh channels. The symbol error performance demonstrates the viability of using neural networks (NN) and DL techniques in communication systems. Finally, a VLC model for on-off keying modulation is implemented. Our extensive experiments verify the effectiveness of our proposed NN architectures.

ISBN: 9781392129456Subjects--Topical Terms:

1567821
Computer Engineering.

Communications Using Deep Learning Techniques.
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300 $a 51 p.
500 $a Source: Masters Abstracts International, Volume: 80-11.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Elgala, Hany.
502 $a Thesis (M.S.)--State University of New York at Albany, 2019.
506 $a This item must not be sold to any third party vendors.
520 $a Deep learning (DL) techniques have the potential of making communication systems more efficient and solving many problems in the physical layer. In this thesis, we proposed an end-to-end communication system for single user and multi-user using a neural network (NN) architecture known as autoencoder (AE). At first, we propose a system which can be deployed in Visible Light Communication (VLC) system where the system is tested in different scenarios using various AE parameters and applied in an indoor VLC model. Symbol error rate (SER) is evaluated with respect to the signal-to-noise ratio (SNR) values at different locations within the room. Then, we extend the work to include multi-user transmission. Additionally, an alternative architecture is introduced for a multi-carrier modulation scheme for radio frequency communication. It is evaluated under additive white Gaussian noise (AWGN) and Rayleigh channels. The symbol error performance demonstrates the viability of using neural networks (NN) and DL techniques in communication systems. Finally, a VLC model for on-off keying modulation is implemented. Our extensive experiments verify the effectiveness of our proposed NN architectures.
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