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DEEP LEARNING FOR FRAME ERROR PROBABILITY PREDICTION IN BICM-OFDM SYSTEMS
KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering. KTH, Dept Informat Sci & Engn, Stockholm, Sweden.;Ericsson Res, Stockholm, Sweden..
KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering. KTH, Dept Informat Sci & Engn, Stockholm, Sweden..ORCID iD: 0000-0001-6630-243X
KTH, School of Electrical Engineering and Computer Science (EECS), Information Science and Engineering. KTH, Dept Informat Sci & Engn, Stockholm, Sweden..ORCID iD: 0000-0002-3599-5584
Ericsson Res, Stockholm, Sweden..
2018 (English)In: 2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), IEEE, 2018, p. 6658-6662Conference paper, Published paper (Refereed)
Abstract [en]

In the context of wireless communications, we propose a deep learning approach to learn the mapping from the instantaneous state of a frequency selective fading channel to the corresponding frame error probability (FEP) for an arbitrary set of transmission parameters. We propose an abstract model of a bit interleaved coded modulation (BICM) orthogonal frequency division multiplexing (OFDM) link chain and show that the maximum likelihood (ML) estimator of the model parameters estimates the true FEP distribution. Further, we exploit deep neural networks as a general purpose tool to implement our model and propose a training scheme for which, even while training with the binary frame error events (i.e., ACKs/NACKs), the network outputs converge to the FEP conditioned on the input channel state. We provide simulation results that demonstrate gains in the FEP prediction accuracy with our approach as compared to the traditional effective exponential SIR metric (EESM) approach for a range of channel code rates, and show that these gains can be exploited to increase the link throughput.

Place, publisher, year, edition, pages
IEEE, 2018. p. 6658-6662
Keywords [en]
FEP, BICM-OFDM, Deep Learning, Neural Networks, Link Adaptation
National Category
Telecommunications
Identifiers
URN: urn:nbn:se:kth:diva-237157DOI: 10.1109/ICASSP.2018.8461864ISI: 000446384606163Scopus ID: 2-s2.0-85054259851OAI: oai:DiVA.org:kth-237157DiVA, id: diva2:1258542
Conference
2018 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP)
Funder
Wallenberg Foundations
Note

QC 20181025

Available from: 2018-10-25 Created: 2018-10-25 Last updated: 2018-10-25Bibliographically approved

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Saxena, ViditJaldén, JoakimBengtsson, Mats

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