The evolution toward sixth generation (6G) wireless networks introduces unprecedented demands on spectral efficiency, ultra-low latency, and real-time adaptability to dynamic radio environments. Central to this transformation is the requirement for intelligent, spectrum-agile radio front ends that can seamlessly operate across heterogeneous frequency bands. Dynamic Spectrum Access (DSA) addresses this challenge by enabling transceivers to autonomously detect, evaluate, and utilize the most suitable spectrum opportunities. The realization of DSA in practical systems, however, requires highly reconfigurable and efficient microwave filtering structures capable of adapting their spectral characteristics in real time. Traditional filter design methodologies, which rely heavily on iterative electromagnetic simulation and manual tuning, fall short in meeting the complexity and multi- objective constraints of modern adaptive RF systems. This paper proposes an integrated design framework incorporating artificial intelligence (AI) optimization, surrogate modeling, and hybrid reconfiguration mechanisms to achieve high-performance micro strip bandpass filters tailored for 6G DSA applications. A deep neural surrogate model is developed to predict filter performance across diverse geometrical and tuning configurations, enabling rapid multi-objective optimization of center frequency, bandwidth, insertion loss, and return loss. Experimental results demonstrate a tuning range from 2.8 to 5.2 GHz with an insertion loss better than 1.4 dB, along with excellent agreement between simulated and fabricated prototypes. The findings establish AI-assisted reconfigurable filtering as a powerful enabler for future 6G-class intelligent radio systems.

6G Networks, Reconfigurable Filters, Microstrip Bandpass Filters, AI Optimization, Surrogate
Modeling, Dynamic Spectrum Access, Varactor Tuning, RF MEMS, Cognitive Radio.

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