Autoencoder-bank based design for adaptive channel-blind robust transmission
Abstract 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 exists an expl...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Springer International Publishing
2021
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| Online Access: | https://hdl.handle.net/1721.1/132038 |
| _version_ | 1826209854371397632 |
|---|---|
| author | Safi, Hossein Akbari, Mohammad Vaezpour, Elaheh Parsaeefard, Saeedeh Shubair, Raed M. |
| author2 | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science |
| author_facet | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Safi, Hossein Akbari, Mohammad Vaezpour, Elaheh Parsaeefard, Saeedeh Shubair, Raed M. |
| author_sort | Safi, Hossein |
| collection | MIT |
| description | Abstract
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 exists an explicit channel model for training that matches the actual channel model in the online transmission. The variation of the actual channel indeed 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. Specifically, we partition channel coefficient values into sub-intervals, train an AE for each partition in the offline phase, and constitute a bank of AEs. Then, based on the actual channel condition in the online phase and the average block error rate (BLER), the optimal pair of encoder and decoder is selected for data transmission. To gain knowledge about the actual channel conditions, we assume a realistic scenario in which the instantaneous channel is not known, and propose to blindly estimate it at the Rx, i.e., without any pilot symbols. Our simulation results confirm the superiority of the proposed adaptive scheme over existing methods in terms of the average power consumption. For instance, when the target average BLER is equal to
$$10^{-4}$$
10
-
4
, our proposed algorithm with 5 pairs of AE can achieve a performance gain over 1.2 dB compared with a non-adaptive scheme. |
| first_indexed | 2024-09-23T14:32:55Z |
| format | Article |
| id | mit-1721.1/132038 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T14:32:55Z |
| publishDate | 2021 |
| publisher | Springer International Publishing |
| record_format | dspace |
| spelling | mit-1721.1/1320382023-02-24T18:11:17Z Autoencoder-bank based design for adaptive channel-blind robust transmission Safi, Hossein Akbari, Mohammad Vaezpour, Elaheh Parsaeefard, Saeedeh Shubair, Raed M. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology. Research Laboratory of Electronics Abstract 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 exists an explicit channel model for training that matches the actual channel model in the online transmission. The variation of the actual channel indeed 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. Specifically, we partition channel coefficient values into sub-intervals, train an AE for each partition in the offline phase, and constitute a bank of AEs. Then, based on the actual channel condition in the online phase and the average block error rate (BLER), the optimal pair of encoder and decoder is selected for data transmission. To gain knowledge about the actual channel conditions, we assume a realistic scenario in which the instantaneous channel is not known, and propose to blindly estimate it at the Rx, i.e., without any pilot symbols. Our simulation results confirm the superiority of the proposed adaptive scheme over existing methods in terms of the average power consumption. For instance, when the target average BLER is equal to $$10^{-4}$$ 10 - 4 , our proposed algorithm with 5 pairs of AE can achieve a performance gain over 1.2 dB compared with a non-adaptive scheme. 2021-09-20T17:41:35Z 2021-09-20T17:41:35Z 2021-03-06 2021-03-07T05:53:12Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/132038 EURASIP Journal on Wireless Communications and Networking. 2021 Mar 06;2021(1):47 PUBLISHER_CC en https://doi.org/10.1186/s13638-021-01929-z Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/ The Author(s) application/pdf Springer International Publishing Springer International Publishing |
| spellingShingle | Safi, Hossein Akbari, Mohammad Vaezpour, Elaheh Parsaeefard, Saeedeh Shubair, Raed M. Autoencoder-bank based design for adaptive channel-blind robust transmission |
| title | Autoencoder-bank based design for adaptive channel-blind robust transmission |
| title_full | Autoencoder-bank based design for adaptive channel-blind robust transmission |
| title_fullStr | Autoencoder-bank based design for adaptive channel-blind robust transmission |
| title_full_unstemmed | Autoencoder-bank based design for adaptive channel-blind robust transmission |
| title_short | Autoencoder-bank based design for adaptive channel-blind robust transmission |
| title_sort | autoencoder bank based design for adaptive channel blind robust transmission |
| url | https://hdl.handle.net/1721.1/132038 |
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