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Mohammad Akbari
ICT Research Institute
Corresponding Author
ORCiD: https://orcid.org/0000-0002-9339-1201
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The idea of employing deep autoencoders (AEs) has been recently proposed to capture the end-to-end performance in the physical layer of communication systems. However, most of the current methods for applying AEs are developed based on the assumption that there is an explicit channel model for training that matches the actual channel model in the online transmission. Since the actual channel varies over time, this imposes a major limitation on employing AE-based systems. In this paper, without relying on an explicit channel model, we propose an adaptive scheme to increase the reliability of an AE-based communication system over different channel conditions. More precisely, we divide the interval of random channel coefficients into n sub-intervals. Subsequently, in the offline training phase, we employ an AE bank consisting of n pairs of encoder and decoder and perform training over the sub-intervals. Then, in the online transmission phase, based on the actual channel conditions, the optimal pair of encoder and decoder is selected for data transmission in terms of satisfying an average block error rate (BLER) constraint imposed on the system. To monitor actual channel conditions for adopting the adaptive scheme, we assume a realistic scenario where the instantaneous channel gain is not known to Tx/Rx and it is blindly estimated at the RX, i.e., without using any pilot symbols. Our simulation results confirms the superiority of the proposed adaptive scheme over a non-adaptive scenario in terms of average power consumption. For instance, when the target average BLER is equal to 10−4 , our proposed algorithm with n = 5 can achieve a performance gain over 1.2 dB compared with a non-adaptive scheme
Keywords
deep learning, autoencoder, blind estimation
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CURRENT STATUS: Published
View the published article at EURASIP Journal on Wireless Communications and Networking.
Version 1
Posted 16 Sep, 2020
No community comments so far
Editorial decision: Major revision
On 13 Nov, 2020
Review #4 received
Received 12 Nov, 2020
Review #3 received
Received 30 Oct, 2020
Review #2 received
Received 25 Oct, 2020
Reviewer #4 agreed
On 07 Oct, 2020
Reviewer #3 agreed
On 28 Sep, 2020
Reviewer #2 agreed
On 28 Sep, 2020
Review #1 received
Received 26 Sep, 2020
Reviewer #1 agreed
On 22 Sep, 2020
Reviewers invited
Invitations sent on 20 Sep, 2020
Editor assigned
On 11 Sep, 2020
First submitted
On 10 Sep, 2020
Submission checks complete
On 10 Sep, 2020
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On 10 Sep, 2020
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Subject Areas
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A new preprint design is coming!
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See the published version of this preprint at EURASIP Journal on Wireless Communications and Networking.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
Mohammad Akbari
ICT Research Institute
Corresponding Author
ORCiD: https://orcid.org/0000-0002-9339-1201
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at EURASIP Journal on Wireless Communications and Networking.
Version 1
Posted 16 Sep, 2020
No community comments so far
Editorial decision: Major revision
On 13 Nov, 2020
Review #4 received
Received 12 Nov, 2020
Review #3 received
Received 30 Oct, 2020
Review #2 received
Received 25 Oct, 2020
Reviewer #4 agreed
On 07 Oct, 2020
Reviewer #3 agreed
On 28 Sep, 2020
Reviewer #2 agreed
On 28 Sep, 2020
Review #1 received
Received 26 Sep, 2020
Reviewer #1 agreed
On 22 Sep, 2020
Reviewers invited
Invitations sent on 20 Sep, 2020
Editor assigned
On 11 Sep, 2020
First submitted
On 10 Sep, 2020
Submission checks complete
On 10 Sep, 2020
Editor invited
On 10 Sep, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Systems and Networking
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at EURASIP Journal on Wireless Communications and Networking.
The idea of employing deep autoencoders (AEs) has been recently proposed to capture the end-to-end performance in the physical layer of communication systems. However, most of the current methods for applying AEs are developed based on the assumption that there is an explicit channel model for training that matches the actual channel model in the online transmission. Since the actual channel varies over time, this imposes a major limitation on employing AE-based systems. In this paper, without relying on an explicit channel model, we propose an adaptive scheme to increase the reliability of an AE-based communication system over different channel conditions. More precisely, we divide the interval of random channel coefficients into n sub-intervals. Subsequently, in the offline training phase, we employ an AE bank consisting of n pairs of encoder and decoder and perform training over the sub-intervals. Then, in the online transmission phase, based on the actual channel conditions, the optimal pair of encoder and decoder is selected for data transmission in terms of satisfying an average block error rate (BLER) constraint imposed on the system. To monitor actual channel conditions for adopting the adaptive scheme, we assume a realistic scenario where the instantaneous channel gain is not known to Tx/Rx and it is blindly estimated at the RX, i.e., without using any pilot symbols. Our simulation results confirms the superiority of the proposed adaptive scheme over a non-adaptive scenario in terms of average power consumption. For instance, when the target average BLER is equal to 10−4 , our proposed algorithm with n = 5 can achieve a performance gain over 1.2 dB compared with a non-adaptive scheme
Figure 1
Figure 2
Figure 3
Figure 4
The full text of this article is available to read as a PDF.
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