Abstract
Recently, deep learning has been applied to many categories of wireless communications. Channel estimation, which plays a critical role inadequate reception and decoding of the information signal has been one of the application domains of learning techniques. Although the training of neural networks to learn through a massive number of dataset samples gains the knowledge to deal with different situations, it comes with a cost for both computational resources and processing time. This paper proposes a combination of transfer learning and meta-learning techniques to overcome this problem. We propose a super-resolution-based channel estimation where transfer learning is used to obtain a good initialization point for new environments while meta-learning enables learning and fast adaptation of different channel tasks to learn using fewer samples. The proposed technique is tested in a vehicular channel environment and the simulation results confirm the improved performance achieved compared to the state-of-the-art techniques.
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Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.
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Funding
This work is done as part of the project “Meta-learning core for Vehicular Networks” funded by the Information Technology Industry Development Agency (ITIDA), Project ID: PRP2019.R27.1.
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Tolba, B., El-Malek, A.H.A., Abo-Zahhad, M. et al. Meta-transfer learning for super-resolution channel estimation. J Ambient Intell Human Comput 14, 2993–3001 (2023). https://doi.org/10.1007/s12652-023-04547-3
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DOI: https://doi.org/10.1007/s12652-023-04547-3