Year: 2026
Type: article
Abstract: Conventional Deep Learning algorithms have had a significant impact on wireless communication, including channel estimation, signal decoding, power optimization, and resource allocation. However, the performance of the Conventional Deep Learning Model is limited when deployed in a dynamic environment due to slow convergence and the requirement for large datasets. To overcome these limitations, a novel Model-Agnostic Meta-Learning (MAML)-based decoding algorithm is developed in this work. The MAML-based algorithm requires fewer training samples and converges faster in new environments. Numerical findings confirm the benefits of employing meta-learning for signal decoding in wireless systems in a real-time scenario, achieving a minimum decoding accuracy of 80% under highly noisy conditions and 100% at moderate SNR. The data required for the proposed model are obtained from a practical setup using the National Instruments Universal Software Radio Peripheral (NI-USRP), and the performance of the proposed model is experimentally verified.
Authors: G. Kalaimagal, M.S. Vasanthi
Institution: SRM Institute of Science and Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7225728231
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