TY  - CONF
N2  - 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.
ID  - scholars11689
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063880108&doi=10.1109%2fICAIIC.2019.8669025&partnerID=40&md5=efcd21cc5d502ef0355d8241b4459b64
EP  - 491
KW  - Artificial intelligence; Decoding; Deep learning; Deep neural networks; Fading channels; Gaussian noise (electronic); Rayleigh fading; White noise; Wireless sensor networks
KW  -  Additive white Gaussian noise channel; Codebook constructions; Flat fading; Flat-fading channels; Machine type communications; Polar codes; Quasi-static Rayleigh fading; Short packets
KW  -  Signal to noise ratio
PB  - Institute of Electrical and Electronics Engineers Inc.
A1  - Irawan, A.
A1  - Witjaksono, G.
A1  - Wibowo, W.K.
SP  - 488
SN  - 9781538678220
N1  - cited By 9; Conference of 1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019 ; Conference Date: 11 February 2019 Through 13 February 2019; Conference Code:146396
AV  - none
TI  - Deep Learning for Polar Codes over Flat Fading Channels
Y1  - 2019///
ER  -