Design and Optimization of Reconfigurable Microwave Filters Using AI Techniques
Keywords:
Artificial intelligence, Bandpass filter, CST simulation, Frequency tuning, Genetic algorithm, Machine learning, Microwave design automation, Neural network, Reconfigurable microwave filter, 5G communicationAbstract
Reconfigurable microwave filters are pivotal components in modern wireless communication systems, enabling dynamic adaptability to varying frequency requirements. Traditional filter design methods often rely on iterative electromagnetic simulations, which are time-consuming and offer limited flexibility. This research presents a novel approach to the design and optimization of reconfigurable microwave filters using artificial intelligence (AI) techniques. A microstrip-based bandpass filter structure is selected, incorporating tunable elements such as varactor diodes to achieve frequency agility. A dataset is generated through parametric simulations conducted in CST and HFSS, covering a broad range of design variables and performance metrics, including center frequency, bandwidth, return loss, and insertion loss.
AI models, including artificial neural networks (ANNs) for response prediction and genetic algorithms (GAs) for parameter optimization, are implemented in Python and MATLAB. The ANN model predicts the filter’s performance metrics with high accuracy, significantly reducing the reliance on full-wave simulations. The GA further refines design parameters to achieve optimal performance across reconfigurable states. The optimized designs are validated through simulation and optionally, through hardware prototyping and vector network analyzer (VNA) measurements.
Results demonstrate that AI-assisted design not only accelerates the development process but also enhances filter performance by enabling intelligent parameter tuning. This methodology paves the way for scalable and adaptive RF/microwave systems suitable for next-generation communication platforms such as 5G, radar, and IoT applications.
References
Hagelauer et al., “From microwave acoustic filters to millimeter-wave operation and new applications,” in IEEE Journal of Microwaves, vol. 3, no. 1, pp. 484-508, Jan. 2023, doi: https://doi.org/10.1109/JMW.2022.3226415
O. Daulay et al., “Ultrahigh dynamic range and low noise figure programmable integrated microwave photonic filter,” Nature Communications, vol. 13, no. 1, Dec. 2022, doi: https://doi.org/10.1038/s41467-022-35485-x
A. Kumar and D. K. Jhariya, “Reconfigurable microwave filters for 5G applications,” MAPAN, vol. 38, no. 4, pp. 1075–1084, Jul. 2023, doi: https://doi.org/10.1007/s12647-023-00670-w
H. Xu, Y. Wang, F. A. Ghaffar and L. Roy, “Reconfigurable microwave filters implemented using field programmable microwave substrate,” in IEEE Transactions on Microwave Theory and Techniques, vol. 69, no. 2, pp. 1344-1354, Feb. 2021, doi: https://doi.org/10.1109/TMTT.2020.3040384
R. H. Kenney, R. E. Jarvis, H. H. Sigmarsson, and J. W. McDaniel, “Efficient time-domain tuning of microwave filters using concepts from the unscented Kalman filter,” in IEEE Transactions on Microwave Theory and Techniques, vol. 72, no. 4, pp. 2391-2413, April 2024, doi: https://doi.org/10.1109/TMTT.2023.3314115
J. K. Rai, P. Ranjan, and R. Chowdhury, “Recent development in reconfigurable dielectric resonator antenna and microwave filter: Design and application,” International Journal of Communication Systems, vol. 37, no. 14, Jun. 2024, doi: https://doi.org/10.1002/dac.5859
J. Ellington, S. Hari and B. A. Floyd, “Improved out-of-band rejection in reflection-Mode N-path filters using tunable transmission zeros,” in IEEE Transactions on Microwave Theory and Techniques, vol. 72, no. 11, pp. 6375-6386, Nov. 2024, doi: https://doi.org/10.1109/TMTT.2024.3395060
W. Zhang, F. Feng and Q. Zhang, “Recent advances in multiphysics optimization for microwave filters using space mapping techniques,” 2021 IEEE MTT-S International Microwave Filter Workshop (IMFW), Perugia, Italy, 2021, pp. 121-123, doi: https://doi.org/10.1109/IMFW49589.2021.9642302
R. Dange, E. Attar, P. Ghodake, and V. Godase, “Smart agriculture automation using ESP8266 Node MCU,” Journal of Electronics Computer Networking and Applied Mathematics, vol. 3, no. 5, pp. 1–9, Sep. 2023, doi: https://doi.org/10.55529/jecnam.35.1.9
V. Godase, “A comprehensive study of revolutionizing EV charging with solar-powered wireless solutions,” Advance Research in Power Electronics and Devices, vol. 2, no. 1, pp. 23-37, Apr. 25, Available: https://matjournals.net/engineering/index.php/ARPED/article/view/1752
V. K. Jamadade, M. G. Ghodke, S. S. Katakdhond, and V. Godase, “A comprehensive review on scalable Arduino radar platform for real-time object detection and mapping”, Journal of Microprocessor and Microcontroller Research, vol. 2, no. 2, pp. 1–11, May 2025, Available: https://matjournals.net/engineering/index.php/JoMMR/article/view/1888
S. K. A., A. Kathiravan, P. Mannam, D. Akila, B. S. Kumar, and V. Vilas Godase, “Advanced neural network models for optimal energy management in microgrids with integrated electric vehicles,” 2025 5th International Conference on Trends in Material Science and Inventive Materials (ICTMIM), Kanyakumari, India, 2025, pp. 1869-1874, doi: https://doi.org/10.1109/ICTMIM65579.2025.10988248
M. Baranowski, Ł. Balewski, A. Lamecki, M. Mrozowski, and J. Galdeano, “The design of cavity resonators and microwave filters applying shape deformation techniques,” in IEEE Transactions on Microwave Theory and Techniques, vol. 71, no. 7, pp. 3065-3074, July 2023, doi: https://doi.org/10.1109/TMTT.2023.3239363
J. Fu, J. Jin, J. Yang, Z. Liu, J. Qian, and H. Lin, “Feature-assisted neural network surrogate-based multiphysics optimization for microwave filters,” in IEEE Microwave and Wireless Technology Letters, vol. 34, no. 5, pp. 474-477, May 2024, doi: https://doi.org/10.1109/LMWT.2024.3374792
V. Godase, P. Pawar, S. Nagane, and S. Kumbhar, “Automatic railway horn system using Node MCU,” Journal of Control & Instrumentation, vol. 15, no. 1, pp. 11-19, May 2024, Available: https://www.researchgate.net/publication/381965272
Y. Yu et al., “State-of-the-Art: AI-assisted surrogate modeling and optimization for microwave filters,” in IEEE Transactions on Microwave Theory and Techniques, vol. 70, no. 11, pp. 4635-4651, Nov. 2022, doi: https://doi.org/10.1109/TMTT.2022.3208898
F. Feng et al., “A novel EM parametric modeling method of microwave filters incorporating multivalued neural networks and transfer functions,” in IEEE Transactions on Microwave Theory and Techniques, vol. 72, no. 11, pp. 6360-6374, Nov. 2024, doi: https://doi.org/10.1109/TMTT.2024.3400152
V. Godase, “Optimized algorithm for face recognition using deepface and multi-task cascaded convolutional network (MTCNN), Optimum Science Journal,” Optimum Science Journal, vol. 3, pp. 66-74, May 2025, Doi: http://doi.org/10.5281/zenodo.15341560
S. Karve, S. Gangonda, G. Birajadar, V. Godase, R. Ghodake, and A. O. Mulani, “Optimized neural network for prediction of neurological disorders,” Emerging Trends in Artificial Intelligence, Data Science and Signal Processing. AIDSP 2023, Communications in Computer and Information Science, pp. 183–191, 2025, doi: https://doi.org/10.1007/978-3-031-88762-8_18
V. V. Godase, Edge AI for smart surveillance: Real-time human activity recognition on low-power devices, International Journal of AI and Machine Learning Innovations in Electronics and Communication Technology, vol. 1, no. 1, pp. 29-46, Jun. 2025, Available: https://matjournals.net/engineering/index.php/IJAIMLECT/article/view/2038
M. Aldrigo et al., “Tunable and miniaturized microwave filters using carbon nanotube-based variable capacitors,” in IEEE Transactions on Nanotechnology, vol. 21, pp. 118-130, 2022, doi: https://doi.org/10.1109/TNANO.2022.3153561
A. Pons-Abenza, A. Álvarez-Melcón, F. D. Quesada-Pereira, A. Romera-Pérez, and L. Arche-Andradas, “Design and implementation of quart-torus waveguide filters for layout optimization using additive manufacturing techniques,” in IEEE Transactions on Microwave Theory and Techniques, vol. 72, no. 2, pp. 1148-1161, Feb. 2024, doi: https://doi.org/10.1109/TMTT.2023.3297717
Yifan Liu, Yuan Yu, Lin Wang, Yu Yu, and Xinliang Zhang, “Reconfigurable microwave photonic bandpass filter based on CROW,” Journal of Lightwave Technology, vol. 42, no. 5, pp. 1597-1604, Oct. 2024, Available: https://opg.optica.org/jlt/abstract.cfm?uri=jlt-42-5-1597
W. Chen, L. -F. Qiu and L. -S. Wu, “Reconfigurable filter with switchable operating frequency, bandwidth, and response type using RF switches and circulators,” in IEEE Transactions on Microwave Theory and Techniques, vol. 73, no. 3, pp. 1708-1716, March 2025, doi: https://doi.org/10.1109/TMTT.2024.3448497
K. Hossain, T. Sabapathy, M. Jusoh, S.-H. Lee, K. S. A. Rahman, and M. R. Kamarudin, “Negative index metamaterial-based frequency-reconfigurable textile CPW antenna for microwave imaging of breast cancer,” Sensors, vol. 22, no. 4, p. 1626, Feb. 2022, doi: https://doi.org/10.3390/s22041626
L. Pantoli, “Special issue on active filters using modern design and optimization techniques,” International Journal of RF and Microwave Computer-Aided Engineering, vol. 30, no. 5, May 2020, doi: https://doi.org/10.1002/mmce.21572
P. Raj, R. Parihar, R. Dhawan, and A. Choudhary, “Energy-efficient bandwidth enhancement of Brillouin microwave photonic bandpass filters,” Optics Express, vol. 30, no. 17, p. 30739, Aug. 2022, doi: https://doi.org/10.1364/oe.465813
K. Li, B. Liu, T. Zhang, P.-L. Chi and T. Yang, “A 16–34 GHz CMOS reconfigurable bandpass filter using mode-switching transformer,” in IEEE Transactions on Microwave Theory and Techniques, vol. 73, no. 3, pp. 1516-1531, March 2025, doi: https://doi.org/10.1109/TMTT.2024.3454126
V. Godase, S. Modi, V. Misal, and S. Kulkarni, “Real-time object detection for autonomous drone navigation using YOLOv8,” Advance Research in Communication Engineering and its Innovations, vol. 2, no. 2, pp. 17-27, Jul. 2025, Available: https://www.researchgate.net/publication/393857258
V. Godase, “Comparative study of ladder logic and structured text programming for PLC,” Journal of Electronics Design and Technology, vol. 2, no. 2, pp. 34-44, Jul. 2025, Available: https://matjournals.net/engineering/index.php/JEDT/article/view/2117
V. Godase, and A. Jagadale, “Three element control using PLC, PID & SCADA interface,” International Journal for Scientific Research & Development, vol. 7, no. 2, pp. 1105-1109, 2019, Available: https://www.ijsrd.com
V. Godase, A. Lawande, K. Mane, K. Davad, and S. Gangonda, “Pipeline survey robot,” International Journal for Scientific Research and Development, vol. 12, no. 3, pp. 141-144, 2024, Available: https://www.researchgate.net/publication/381282147
A. Vishwakarma, A. Sahu, N. Sheikh, P. Payasi, S. K. Rajput, and L. Srivastava, “IoT-based greenhouse monitoring and controlling system,” 2020 IEEE Students Conference on Engineering & Systems (SCES), Prayagraj, India, 2020, pp. 1-6, doi: https://doi.org/10.1109/SCES50439.2020.9236693
R. S. Pol, Dr. Manoj V. Bhalerao, and A. O. Mulani, “A real-time IoT-based System Prediction and Monitoring of Landslides,” International Journal of Food and Nutritional Sciences, vol. 11, no. 5, pp. 5204-5223, Jul. 2022, Available: https://ijfans.org/issue-content/a-real-time-iot-based-system-prediction-and-monitoring-of-landslides-10436
A. O. Mulani et al., “ML-powered internet of medical things structure for heart disease prediction,” Journal of Pharmacology and Pharmacotherapeutics, Jan. 2025, doi: https://doi.org/10.1177/0976500x241306184
Santosh Aiwale et al., “Noninvasive anemia detection and prediagnosis,” Journal of Pharmacology and Pharmacotherapeutics, vol. 15, no. 4, pp. 408-416, Oct. 2024, doi: https://doi.org/10.1177/0976500x241276307
S. P. Thigale, H. M. Jadhav, A. O. Mulani, G. B. Birajadar, M. Nagrale, and Mohini P. Sardey, “Internet of things and robotics in transforming healthcare services,” Afr J Biol Sci (S Afr), vol. 6, no. 6, pp. 1567-1575, May 2024, Doi: https://doi.org/10.33472/AFJBS.6.6.2024.1567-1575
Rahul S. Pol, Altaf O. Mulani, and Manoj V. Bhalerao, “Cloud-based memory-efficient biometric attendance system using face recognition,” Stochastic Modeling & Applications, vol. 25, no. 2, pp. 403-416, Dec. 2021, Available: https://www.researchgate.net/publication/382531574
V. Godase, A. Mulani, R. Ghodak, G. Birajadar, S. Takale, and M. Kolte, “A MapReduce and Kalman Filter based secure IIoT environment in Hadoop,” Sanshodhak, vol. 25, pp. 38-47, Jun. 2024, Available: https://www.researchgate.net/publication/383941977
A. J. Mandwale and A. O. Mulani, “Different approaches for implementation of Viterbi decoder on reconfigurable platform,” 2015 International Conference on Pervasive Computing (ICPC), Pune, India, 2015, pp. 1-4, doi: https://doi.org/10.1109/PERVASIVE.2015.7086976
Godase, A. Mulani, A. Pawar, and K. Sahani, “A comprehensive review on PIR sensor-based light automation systems,” International Journal of Image Processing and Smart Sensors, vol. 1, no.1, pp. 22-29, May 2025, Available: https://www.researchgate.net/publication/391462475
