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Artificial Intelligence and Machine Learning Techniques for Microwave Technologies
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Artificial Intelligence and Machine Learning Techniques for Microwave Technologies

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microwave and Wireless Communications".

Deadline for manuscript submissions: closed (15 April 2025) | Viewed by 7682

Special Issue Editors

Dr. Pere L. Gilabert

E-Mail Website
Guest Editor
Department of Signal Theory and Communications, Universitat Politècnica de Catalunya, 08860 Castelldefels, Spain
Interests: digital signal processing techniques for emerging wireless and efficient transmitter technologies
Special Issues, Collections and Topics in MDPI journals
Dr. Telmo Cunha

E-Mail Website
Guest Editor
Department of Electronics, Telecommunications and Informatics, Universidade de Aveiro—Instituto de Telecomunicações, 3810-193 Aveiro, Portugal
Interests: application of system-level modeling and system identification techniques for improving the performance of wireless transmitters
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) and machine learning (ML) technologies have revolutionized various industries, demonstrating their potential to enhance efficiency, accuracy, and innovation. From optimizing microwave circuits to advancing signal processing techniques, AI and ML are indeed paving the way for making unprecedented advancements in the field. The potential applications are vast and varied, encompassing a wide range of topics. In this context, we invite researchers to contribute their original research and review articles to this Special Issue dedicated to "AI and ML Technologies for Microwave Applications".

Topics of interest for this Special Issue include, but are not limited to, the following:

  • AI-Optimized Microwave Circuit Design: Utilizing machine learning algorithms to optimize the design and performance of microwave circuits, including filters, amplifiers, and oscillators.
  • Machine Learning for Microwave Imaging and Sensing: Exploring the use of AI techniques for improving the resolution, speed, and accuracy of microwave imaging and sensing systems, with applications in remote sensing, medical imaging, and security.
  • Deep Learning Techniques for Radar Signal Processing: Investigating the application of deep learning algorithms for radar signal processing tasks such as target detection, classification, and tracking in both civilian and military contexts.
  • AI-Driven Beamforming and Antenna Array Optimization: Leveraging artificial intelligence to optimize the design and operation of antenna arrays for beamforming, direction finding, and communication in wireless systems.
  • Neural-Network-Based Microwave Communication Systems: Designing and implementing communication systems that utilize neural networks for modulation, demodulation, channel equalization, linearization, and interference mitigation in microwave frequency bands.
  • Reinforcement Learning in Microwave System Control: Applying reinforcement learning techniques to optimize the operation and control of microwave systems, including adaptive beamforming, power control, and resource allocation.
  • AI-Enhanced Electromagnetic Simulation and Modeling: Enhancing electromagnetic simulation and modeling tools with AI capabilities to enable faster and more accurate analysis of microwave devices, antennas, and propagation phenomena.
  • ML Applications in Microwave Medical Diagnostics and Treatments: Investigating the use of machine learning algorithms for improving medical diagnostics and treatments using microwave technologies, such as microwave imaging for breast cancer detection and microwave ablation for tumor therapy.
  • Cognitive Radio Systems Empowered by AI Algorithms: Developing cognitive radio systems that utilize AI algorithms for spectrum sensing, dynamic spectrum access, and interference management to optimize spectrum utilization and enhance communication efficiency.
  • AI-Driven Optimization of Microwave Power Amplifiers: Employing artificial intelligence techniques to optimize the design and linearization performance of microwave power amplifiers for applications in wireless communication, radar, and satellite systems.

Dr. Pere L. Gilabert
Dr. Telmo Cunha
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • machine learning
  • microwave technologies
  • wireless communications
  • radar
  • satellite systems

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Published Papers (6 papers)

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Research

16 pages, 630 KiB  
Article
A Study on Performance Improvement of Maritime Wireless Communication Using Dynamic Power Control with Tethered Balloons
by Tao Fang, Jun-han Wang, Jaesang Cha, Incheol Jeong and Chang-Jun Ahn
Electronics 2025, 14(7), 1277; https://doi.org/10.3390/electronics14071277 - 24 Mar 2025
Viewed by 214
Abstract
In recent years, the demand for maritime wireless communication has been increasing, particularly in areas such as ship operations management, marine resource utilization, and safety assurance. However, due to the difficulty of deploying base stations(BSs), maritime communication still faces challenges in terms of [...] Read more.
In recent years, the demand for maritime wireless communication has been increasing, particularly in areas such as ship operations management, marine resource utilization, and safety assurance. However, due to the difficulty of deploying base stations(BSs), maritime communication still faces challenges in terms of limited coverage and unreliable communication quality. As the number of users on ships and offshore platforms increases, along with the growing demand for mobile communication at sea, conventional terrestrial base stations struggle to provide stable connectivity. Therefore, existing maritime communication primarily relies on satellite communication and long-range Wi-Fi. However, these solutions still have limitations in terms of cost, stability, and communication efficiency. Satellite communication solutions, such as Starlink and Iridium, provide global coverage and high reliability, making them essential for deep-sea and offshore communication. However, these systems have high operational costs and limited bandwidth per user, making them impractical for cost-sensitive nearshore communication. Additionally, geostationary satellites suffer from high latency, while low Earth orbit (LEO) satellite networks require specialized and expensive terminals, increasing hardware costs and limiting compatibility with existing maritime communication systems. On the other hand, 5G-based maritime communication offers high data rates and low latency, but its infrastructure deployment is demanding, requiring offshore base stations, relay networks, and high-frequency mmWave (millimeter-wave) technology. The high costs of deployment and maintenance restrict the feasibility of 5G networks for large-scale nearshore environments. Furthermore, in dynamic maritime environments, maintaining stable backhaul connections presents a significant challenge. To address these issues, this paper proposes a low-cost nearshore wireless communication solution utilizing tethered balloons as coastal base stations. Unlike satellite communication, which relies on expensive global infrastructure, or 5G networks, which require extensive offshore base station deployment, the proposed method provides a more economical and flexible nearshore communication alternative. The tethered balloon is physically connected to the coast, ensuring stable power supply and data backhaul while providing wide-area coverage to support communication for ships and offshore platforms. Compared to short-range communication solutions, this method reduces operational costs while significantly improving communication efficiency, making it suitable for scenarios where global satellite coverage is unnecessary and 5G infrastructure is impractical. Additionally, conventional uniform power allocation or channel-gain-based amplification methods often fail to meet the communication demands of dynamic maritime environments. This paper introduces a nonlinear dynamic power allocation method based on channel gain information to maximize downlink communication efficiency. Simulation results demonstrate that, compared to conventional methods, the proposed approach significantly improves downlink communication performance, verifying its feasibility in achieving efficient and stable communication in nearshore environments. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Techniques for Microwave Technologies)
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13 pages, 3477 KiB  
Article
Machine Learning-Driven Approaches for Advanced Microwave Filter Design
by Sara Javadi, Behrooz Rezaee, Sayyid Shahab Nabavi, Michael Ernst Gadringer and Wolfgang Bösch
Electronics 2025, 14(2), 367; https://doi.org/10.3390/electronics14020367 - 17 Jan 2025
Viewed by 1076
Abstract
This study introduces a machine learning (ML)-driven approach to next-generation microwave filter design that enhances both accuracy and efficiency via repeated refinement. The approach includes generating a coupling matrix from filter specifications, followed by predicting physical parameters such as iris widths and resonator [...] Read more.
This study introduces a machine learning (ML)-driven approach to next-generation microwave filter design that enhances both accuracy and efficiency via repeated refinement. The approach includes generating a coupling matrix from filter specifications, followed by predicting physical parameters such as iris widths and resonator lengths using ML models, especially with the XGBoost algorithm. These predictions are validated and tuned via simulations and iterative adjustments to ensure meeting the performance criteria, such as center frequency, bandwidth, and return loss. For tuning, in this work, we used Simulated Annealing to extract a coupling matrix to reduce errors and hence allow accurate further optimization. The predicted values before optimization are more than 90 percent accurate compared to the optimized values, significantly reducing the optimization time and the number of iterations required. To demonstrate the procedure’s validity, third-, fourth-, and fifth-order filters are implemented, which shows significant improvements in design efficiency and accuracy. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Techniques for Microwave Technologies)
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21 pages, 1422 KiB  
Article
Multi-Agent Reinforcement Learning for Efficient Resource Allocation in Internet of Vehicles
by Jun-Han Wang, He He, Jaesang Cha, Incheol Jeong and Chang-Jun Ahn
Electronics 2025, 14(1), 192; https://doi.org/10.3390/electronics14010192 - 5 Jan 2025
Viewed by 1417
Abstract
The Internet of Vehicles (IoV), a burgeoning technology, merges advancements in the internet, vehicle electronics, and wireless communications to foster intelligent vehicle interactions, thereby enhancing the efficiency and safety of transportation systems. Nonetheless, the continual and high-frequency communications among vehicles, coupled with regional [...] Read more.
The Internet of Vehicles (IoV), a burgeoning technology, merges advancements in the internet, vehicle electronics, and wireless communications to foster intelligent vehicle interactions, thereby enhancing the efficiency and safety of transportation systems. Nonetheless, the continual and high-frequency communications among vehicles, coupled with regional limitations in system capacity, precipitate significant challenges in allocating wireless resources for vehicular networks. In addressing these challenges, this study formulates the resource allocation issue as a multi-agent deep reinforcement learning scenario and introduces a novel multi-agent actor-critic framework. This framework incorporates a prioritized experience replay mechanism focused on distributed execution, which facilitates decentralized computing by structuring the training processes and defining specific reward functions, thus optimizing resource allocation. Furthermore, the framework prioritizes empirical data during the training phase based on the temporal difference error (TD error), selectively updating the network with high-priority data at each sampling point. This strategy not only accelerates model convergence but also enhances the learning efficacy. The empirical validations confirm that our algorithm augments the total capacity of vehicle-to-infrastructure (V2I) links by 9.36% and the success rate of vehicle-to-vehicle (V2V) transmissions by 6.74% compared with a benchmark algorithm. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Techniques for Microwave Technologies)
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11 pages, 1798 KiB  
Article
Whale Optimization Algorithm with Machine Learning for Microwave Imaging
by Chien-Ching Chiu, Ching-Lieh Li, Po-Hsiang Chen, Hung-Ming Cheng and Hao Jiang
Electronics 2024, 13(22), 4342; https://doi.org/10.3390/electronics13224342 - 5 Nov 2024
Viewed by 1125
Abstract
This paper introduces a novel approach for reconstructing microwave imaging by combining the Whale Optimization Algorithm (WOA) with deep learning techniques. In it, electromagnetic waves are used to illuminate inhomogeneous dielectric objects in free space, and the scattered field is recorded. Due to [...] Read more.
This paper introduces a novel approach for reconstructing microwave imaging by combining the Whale Optimization Algorithm (WOA) with deep learning techniques. In it, electromagnetic waves are used to illuminate inhomogeneous dielectric objects in free space, and the scattered field is recorded. Due to the highly nonlinear nature of microwave imaging, the WOA is first employed to calculate an initial guess from the measured scattered field of dielectric objects. This step significantly reduces the training complexity for machine learning. Subsequently, the initial guess provided by the WOA is fed into a U-Net to accurately reconstruct the microwave image. Numerical simulation results indicate that the combination of the WOA and machine learning outperforms traditional methods under varying noise levels, enhancing the precision and effectiveness of the reconstruction process. In detail, the RMSE can be reduced 4–10% for dielectric constant distribution from 1 to 2.5 and SSIM can be increased about 30% for most cases. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Techniques for Microwave Technologies)
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21 pages, 6252 KiB  
Article
HCTC: Hybrid Convolutional Transformer Classifier for Automatic Modulation Recognition
by Jayesh Deorao Ruikar, Do-Hyun Park, Soon-Young Kwon and Hyoung-Nam Kim
Electronics 2024, 13(19), 3969; https://doi.org/10.3390/electronics13193969 - 9 Oct 2024
Cited by 1 | Viewed by 1214
Abstract
Automatic modulation recognition (AMR) methods used in advanced wireless communications systems can identify unknown signals without requiring reference information. However, the acceptance of these methods depends on the accuracy, number of parameters, and computational complexity. This study proposes a hybrid convolutional transformer classifier [...] Read more.
Automatic modulation recognition (AMR) methods used in advanced wireless communications systems can identify unknown signals without requiring reference information. However, the acceptance of these methods depends on the accuracy, number of parameters, and computational complexity. This study proposes a hybrid convolutional transformer classifier (HCTC) for the classification of unknown signals. The proposed method utilizes a three-stage framework to extract features from in-phase/quadrature (I/Q) signals. In the first stage, spatial features are extracted using a convolutional layer. In the second stage, temporal features are extracted using a transformer encoder. In the final stage, the features are mapped using a deep-learning network. The proposed HCTC method is investigated using the benchmark RadioML database and compared with state-of-the-art methods. The experimental results demonstrate that the proposed method achieves a better performance in modulation signal classification. Additionally, the performance of the proposed method is evaluated when applied to different batch sizes and model configurations. Finally, open issues in modulation recognition research are addressed, and future research perspectives are discussed. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Techniques for Microwave Technologies)
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21 pages, 3979 KiB  
Article
Modeling, Design, and Application of Analog Pre-Distortion for the Linearity and Efficiency Enhancement of a K-Band Power Amplifier
by Tommaso Cappello, Sarmad Ozan, Andy Tucker, Peter Krier, Tudor Williams and Kevin Morris
Electronics 2024, 13(19), 3818; https://doi.org/10.3390/electronics13193818 - 27 Sep 2024
Cited by 1 | Viewed by 1248
Abstract
This paper presents the theory, design, and application of a dual-branch series-diode analog pre-distortion (APD) linearizer to improve the linearity and efficiency of a K-band high-power amplifier (HPA). A first-of-its-kind, frequency-dependent large-signal APD model is presented. This model is used to evaluate different [...] Read more.
This paper presents the theory, design, and application of a dual-branch series-diode analog pre-distortion (APD) linearizer to improve the linearity and efficiency of a K-band high-power amplifier (HPA). A first-of-its-kind, frequency-dependent large-signal APD model is presented. This model is used to evaluate different phase relationships between the linear and nonlinear branches, suggesting independent gain and phase expansion characteristics with this topology. This model is used to assess the impact of diode resistance, capacitance, and ideality factors on the APD characteristics. This feature is showcased with two similar GaAs diodes to find the best fit for the considered HPA. The selected diode is characterized and modeled between 1 and 26.5 GHz. A comprehensive APD design and simulation workflow is reported. Before fabrication, the simulated APD is evaluated with the measured HPA to verify linearity improvements. The APD prototype achieves a large-signal bandwidth of 6 GHz with 3 dB gain expansion and 8° phase rotation. This linearizer is demonstrated with a 17–21 GHz GaN HPA with 41 dBm output power and 35% efficiency. Using a wideband 750 MHz signal, this APD improves the noise–power ratio (NPR) by 6.5–8.2 dB over the whole HPA bandwidth. Next, the HPA output power is swept to compare APD vs. power backoff for the same NPR. APD improves the HPA output power by 1–2 W and efficiency by approximately 5–9% at 19 GHz. This efficiency improvement decreases by only 1–2% when including the APD post-amplifier consumption, thus suggesting overall efficiency and output power improvements with APD at K-band frequencies. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Techniques for Microwave Technologies)
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