International Journal of Engineering
Trends and Technology

Research Article | Open Access | Download PDF
Volume 74 | Issue 10 | Year 2026 | Article Id. IJETT-V74I10P108 | DOI : https://doi.org/10.14445/22315381/IJETT-V74I10P108

Model Agnostic Meta-Learning based Adaptive Decoder-A Cognitive Radio Approach

G.Kalaimagal, M.S.Vasanthi

Received Revised Accepted Published
19 Feb 2026 28 Aug 2026 29 Aug 2026 27 Oct 2026

Citation :

G.Kalaimagal, M.S.Vasanthi, "Model Agnostic Meta-Learning based Adaptive Decoder-A Cognitive Radio Approach," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 10, pp. 95-109, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I10P108

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.

Keywords

Meta Learning, Signal decoding, Model-Agnostic, Accuracy, Data-Driven Learning, NI-Usrp.

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