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Model-aided distributed shallow learning for OFDM receiver in IEEE 802.11 channel model

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Abstract

Deep learning (DL) has been recognized as an instrumental tool for the design of future communication systems. Since it is still not clear whether a fully data-driven end-to-end communication learning approach would eventually outperform the traditional ones in terms of performance and complexity, it is argued that the optimal design needs to be tackled by taking the benefits of both model-based and data-driven approaches and by leveraging the concept of transfer learning. However, the grand question lies in how this can be implemented efficiently. As such, this paper proposes an efficient end-to-end OFDM based receiver learning approach based on distributed data-driven and model-based approaches. The approach relies mainly on augmenting a typical OFDM receiver’s processing blocks with a shallow neural network as a data-driven stub to improve its performance. Relying on a two-phases training approach, the last receiver’s processing stage benefits from the transfer learning approach to improve its performance. Limiting the scope to a typical OFDM transmission where the DL-based methods fail, the proposed model-aided shallow learning receiver shows performance improvements compared to the baseline structure.

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Notes

  1. So far, recent results from [3, 11, 19] reveal counter intuitive facts for the linear OFDM case where the baseline model is hard to outperform. The linear case is defined as a case where there is no noticeable clipping noise and/or the removal of the cyclic prefix. All wireless systems (e.g. 4G, 5G and IEEE 802.11) operates under such quasi-linear case because the clipping noise is almost minimized using predistortion techniques that shall provide better than 45 dB adjacent channel leakage ratio and low error vector magnitude (EVM). All OFDM waveforms adopted in such technologies are CP based as well.

  2. The partitioning of the dataset to a training set and a test set is a deeply rooted practice using a typical ratio of 20–30% for the test set. We have in fact tried several partitioning ratios within such a range but the results are not worth mentioning as they do not add any insights.

  3. For comparison, it is not trivial to reproduce the results in [21, 18, 19] using our IEEE 802.11 transmission model. In fact, it is time consuming to fine-tune the hyper parameters of such models and one may end up not providing a faire implementation of those techniques. We therefore rely on the baseline models which are proven to be robust and widely used as a reference method.

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Acknowledgements

This work has been funded by the Natural Sciences and Engineering Research Council of Canada, Canadian Foundation for Innovation, and the CMC Microsystems. The authors would also like to thank Philippe Massicotte for his help in the CPU/GPU setups.

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  1. Laboratoire des Signaux et Systèmes Intégrés, Department of Electrical and Computer Engineering, Université du Québec à Trois-Rivières, 3351, Boul. des Forges, Trois-Rivières, QC, Canada

    Messaoud Ahmed Ouameur, Anh Duong Tuấn Lê & Daniel Massicotte

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  1. Messaoud Ahmed Ouameur
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  2. Anh Duong Tuấn Lê
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  3. Daniel Massicotte
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Correspondence to Daniel Massicotte.

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D. Massicotte holds the Chaire de recherche sur les signaux et l’intelligence des systèmes haute performance.

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Ahmed Ouameur, M., Lê, A.D.T. & Massicotte, D. Model-aided distributed shallow learning for OFDM receiver in IEEE 802.11 channel model. Wireless Netw 26, 5427–5436 (2020). https://doi.org/10.1007/s11276-020-02412-1

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