AI-Driven RF Co-Design: Antennas to Baseband
DOI:
https://doi.org/10.15662/IJRAI.2025.0801001Keywords:
RF Co-Design, Antenna Design, Baseband Processing, Artificial Intelligence, Machine Learning, Deep Reinforcement Learning, Massive MIMO, Electromagnetic Simulation, Surrogate Models, Adaptive SystemsAbstract
The design of radio frequency (RF) systems, spanning antennas to baseband processing, traditionally involves compartmentalized optimization of individual components. However, the increasing complexity of wireless communication standards, coupled with the demand for miniaturization and higher efficiency, calls for an integrated co-design approach. Recent advances in artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), have catalyzed new methodologies for RF system co-design, enabling joint optimization of antennas, RF front-ends, and baseband algorithms. This paper explores AI-driven RF co-design frameworks that leverage data-driven models to optimize multiple system layers simultaneously. AI techniques facilitate rapid exploration of high-dimensional design spaces and capture complex nonlinear relationships among antenna geometries, circuit parameters, and baseband signal processing algorithms. By incorporating reinforcement learning, generative models, and surrogate-assisted optimization, designers can significantly reduce time-to-market while improving performance metrics such as gain, bandwidth, energy efficiency, and bit error rate. We review state-of-the-art AI methodologies applied in antenna design, RF circuit tuning, and adaptive baseband processing. A case study is presented where a deep reinforcement learning agent jointly optimizes a compact antenna array and a baseband precoding algorithm for a massive MIMO system, demonstrating superior spectral efficiency and reduced hardware complexity compared to conventional decoupled design. Our research methodology includes dataset generation via electromagnetic simulation tools, training AI models for parameter prediction, and real-world hardware validation. Results confirm that AI-driven co-design enables more flexible and robust RF systems, adapting to dynamic channel conditions and hardware impairments. Challenges such as data scarcity, model interpretability, and computational overhead are discussed. The paper concludes by outlining future directions including federated AI for distributed RF design and explainable AI to enhance trustworthiness.
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