Using Sequence-to-Sequence Models for Carrier Frequency Offset Estimation of Short Messages and Chaotic Maps
Article
Article Title | Using Sequence-to-Sequence Models for Carrier Frequency Offset Estimation of Short Messages and Chaotic Maps |
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ERA Journal ID | 210567 |
Article Category | Article |
Authors | Davey, Christopher P. (Author), Shakeel, Ismail (Author), Deo, Ravinesh C. (Author), Salcedo-sanz, Sancho and Soar, Jeffrey (Author) |
Journal Title | IEEE Access |
Journal Citation | 10, pp. 119814 - 119825 |
Number of Pages | 13 |
Year | 2022 |
Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
Place of Publication | United States |
ISSN | 2169-3536 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/ACCESS.2022.3221762 |
Web Address (URL) | https://ieeexplore.ieee.org/document/9947074 |
Abstract | Deep Learning methods have produced good carrier frequency offset estimations for short message sequences in comparison with methods based on the Fast Fourier Transform. However, these performance gains were observed for short ranges of frequency offsets, sequences with predefined pilot symbols and periodic modulation schemes. Chaotic modulation has an advantage over periodic signals in offering security through the continuous changes produced by parameterising the chaotic map function. However, synchronisation of chaotic map parameters in coherent receivers is dependent on the carrier recovery of phase and frequency which dramatically reduces the demodulation performance under high noise levels. This article presents a stacked sequence-to-sequence neural network architecture for blind carrier frequency offset estimation of both periodic and chaotic modulation schemes. The results obtained demonstrate better performance than conventional methods in low SNR for the Additive White Gaussian Noise channel. While this technique operates without feature engineering, the results demonstrate that data augmentation produces a higher degree of accuracy for such models, indicating the benefit of integration With conventional signal pre-processing steps as part of the deep learning pipeline. The proposed neural network architecture is shown to perform carrier frequency offset estimation, not only for the selected periodic modulations, but also in the case of highly non-linear chaotic maps. This suggests the applicability of deep learning methods for synchronisation in waveforms that employ chaotic modulation schemes for secure communication and for applications where short and sporadic messaging are required (e.g., Internet of Things). |
Keywords | Chaotic communication; Deep learning; Fast Fourier transforms; Frequency synchronisation; Carrier frequency offset estimation |
Related Output | |
Is part of | Applied Deep Learning for Artificial Intelligence-enabled Wireless Communication |
ANZSRC Field of Research 2020 | 460207. Modelling and simulation |
400608. Wireless communication systems and technologies (incl. microwave and millimetrewave) | |
Public Notes | This article is part of a UniSQ Thesis by publication. See Related Output. |
Byline Affiliations | School of Mathematics, Physics and Computing |
University of Alcala, Spain | |
School of Business | |
Institution of Origin | University of Southern Queensland |
Funding source | Grant ID Department of Defence |
https://research.usq.edu.au/item/q7wz7/using-sequence-to-sequence-models-for-carrier-frequency-offset-estimation-of-short-messages-and-chaotic-maps
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