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Short Communication Volume 8 Issue 1
AI/ML applications in RF design: PA linearization, spectrum sensing, and component modeling
Bhargava Teja Nukala, Santosh Kota, Sunil Nagaraj
Received: July 18, 2025 | Published: August 6, 2025
Citation: Nukala BT, Kota S, Nagaraj S. AI/ML applications in RF design: PA linearization, spectrum sensing, and component modeling. Open Access J Sci. 2025;8(1):144-145. DOI: 10.15406/oajs.2025.08.00257
Abstract
Recent advances in artificial intelligence (AI) and machine learning (ML) are transforming radio frequency (RF) design and system optimization. This paper reviews key applications of AI/ML techniques in three critical RF domains: power amplifier (PA) linearization, spectrum sensing, and passive component modeling. These applications demonstrate the ability of data-driven models to enhance performance, reduce design complexity, and enable adaptive behavior in real-time RF systems.
Keywords: data-driven models, system optimization, AI, ML, memory polynomial, neural networks
©2025 Nukala, et al. This is an open access article distributed under the terms of the, which permits unrestricted use, distribution, and build upon your work non-commercially.