Deep Learning for Polar Codes over Flat Fading Channels
This paper proposes a deep-neural-networks scheme for decoding polar coded short packets. We consider packet transmission over frequency-flat quasi-static Rayleigh fading channels, where the channel coefficient is constant over a packet but changes packet-by-packet. Potential applications of the pro...
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| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2019
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063880108&doi=10.1109%2fICAIIC.2019.8669025&partnerID=40&md5=efcd21cc5d502ef0355d8241b4459b64 http://eprints.utp.edu.my/23564/ |
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| Institution: | Universiti Teknologi Petronas |
| Summary: | This paper proposes a deep-neural-networks scheme for decoding polar coded short packets. We consider packet transmission over frequency-flat quasi-static Rayleigh fading channels, where the channel coefficient is constant over a packet but changes packet-by-packet. Potential applications of the proposed technique are machine-Type communications, messaging services, smart metering networks, and other wireless sensor networks requiring high reliability and low-latency. Computer simulations results confirm that even with simple codebook construction for an additive white Gaussian noise (AWGN) channel without fading, the proposed technique closes to the theoretical outage and achieves the coding gain in fading channel. Analyses of the learning epochs and training signal-To-noise power ratio (SNR) selections are also presented to demonstrate the effectiveness of the technique. © 2019 IEEE. |
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