In the rapidly evolving field of technology, the integration of artificial intelligence (AI) technologies is pioneering new efficiencies in antenna systems, particularly within the VHF (Very High Frequency) and UHF (Ultra High Frequency) bands. This call for papers seeks innovative research on developing and applying AI-driven techniques to enhance RF signal transmission and reception by introducing advanced monitoring, control, and optimization strategies for VHF and UHF antennas. Submissions are invited from researchers working on groundbreaking methods to leverage AI to improve the performance of these critical communication components.
Our focus on advancing real-time control, measurement accuracy, and visualization of key metrics like forward power and VSWR aligns with AI-driven antenna management goals. These improvements are crucial for integrating AI into antenna systems, allowing for autonomous adjustments, precise monitoring, and data visualization for informed decisions. This commitment highlights AI's role in enhancing antenna management, promising greater efficiency, reliability, and adaptability in communication networks. The scope of this special issue extends to intelligent monitoring systems, AI-driven system control, advanced signal processing, predictive maintenance, and energy optimization strategies. We aim to integrate these modern technologies with traditional antenna system management approaches to achieve superior operational efficiency and performance.
Contributors are encouraged to submit their work to 'Frontiers in Antennas and Propagation,' participating in a pivotal discourse that aims to transform antenna systems through the advanced application of AI technologies. Areas of interest include but are not limited to, real-time management of antenna components, AI in signal condition adaptation, and the creation of predictive models for antenna system performance.
Join us in leading the charge towards a future where antenna systems operate at unparalleled levels of efficiency, guided by the latest advancements in AI and computational technologies.
The scope for the research topic aim to showcase the intersection of AI with antenna system efficiency. These topics include developing AI-driven monitoring and control systems, advanced signal processing methods, predictive maintenance, and integrating AI with traditional optimization methods to enhance antenna performance and management.
Topics include but are not limited to:
• Advanced Intelligent Antenna Monitoring Systems:
Focus on creating and implementing systems for comprehensive monitoring of antenna operations, emphasizing real-time management and optimization of key components like diplexers and distributors using AI.
• AI-Integrated Smart Antenna System Management and Optimization:
This approach uses AI to make antenna systems more efficient and self-improving, dynamically managing and optimizing to meet modern communication needs.
• Synergizing AI with Traditional Optimization Techniques:
Integrating AI with conventional methods to achieve unprecedented system optimization and control in antenna operations.
• Leveraging Multimodal Information for Antenna Systems:
Address the complexities of gathering and using multimodal information to improve antenna system design and functionality.
• Development of Robust Predictive Models for Antenna Performance:
Acquiring accurate and reliable predictive models to forecast antenna system performance and efficiency, facilitated by AI and machine learning.
• Advancements in theory and practice for improved antenna system management:
Enhancing antenna system management through theoretical and practical advancements ensures these systems meet modern communication demands with exceptional efficiency and adaptability.
Keywords:
AI in Antenna System Control, intelligent antenna monitoring systems, Signal Processing Techniques, Predictive Maintenance and Demand Forecasting, Integration of AI with Traditional Methods
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
In the rapidly evolving field of technology, the integration of artificial intelligence (AI) technologies is pioneering new efficiencies in antenna systems, particularly within the VHF (Very High Frequency) and UHF (Ultra High Frequency) bands. This call for papers seeks innovative research on developing and applying AI-driven techniques to enhance RF signal transmission and reception by introducing advanced monitoring, control, and optimization strategies for VHF and UHF antennas. Submissions are invited from researchers working on groundbreaking methods to leverage AI to improve the performance of these critical communication components.
Our focus on advancing real-time control, measurement accuracy, and visualization of key metrics like forward power and VSWR aligns with AI-driven antenna management goals. These improvements are crucial for integrating AI into antenna systems, allowing for autonomous adjustments, precise monitoring, and data visualization for informed decisions. This commitment highlights AI's role in enhancing antenna management, promising greater efficiency, reliability, and adaptability in communication networks. The scope of this special issue extends to intelligent monitoring systems, AI-driven system control, advanced signal processing, predictive maintenance, and energy optimization strategies. We aim to integrate these modern technologies with traditional antenna system management approaches to achieve superior operational efficiency and performance.
Contributors are encouraged to submit their work to 'Frontiers in Antennas and Propagation,' participating in a pivotal discourse that aims to transform antenna systems through the advanced application of AI technologies. Areas of interest include but are not limited to, real-time management of antenna components, AI in signal condition adaptation, and the creation of predictive models for antenna system performance.
Join us in leading the charge towards a future where antenna systems operate at unparalleled levels of efficiency, guided by the latest advancements in AI and computational technologies.
The scope for the research topic aim to showcase the intersection of AI with antenna system efficiency. These topics include developing AI-driven monitoring and control systems, advanced signal processing methods, predictive maintenance, and integrating AI with traditional optimization methods to enhance antenna performance and management.
Topics include but are not limited to:
• Advanced Intelligent Antenna Monitoring Systems:
Focus on creating and implementing systems for comprehensive monitoring of antenna operations, emphasizing real-time management and optimization of key components like diplexers and distributors using AI.
• AI-Integrated Smart Antenna System Management and Optimization:
This approach uses AI to make antenna systems more efficient and self-improving, dynamically managing and optimizing to meet modern communication needs.
• Synergizing AI with Traditional Optimization Techniques:
Integrating AI with conventional methods to achieve unprecedented system optimization and control in antenna operations.
• Leveraging Multimodal Information for Antenna Systems:
Address the complexities of gathering and using multimodal information to improve antenna system design and functionality.
• Development of Robust Predictive Models for Antenna Performance:
Acquiring accurate and reliable predictive models to forecast antenna system performance and efficiency, facilitated by AI and machine learning.
• Advancements in theory and practice for improved antenna system management:
Enhancing antenna system management through theoretical and practical advancements ensures these systems meet modern communication demands with exceptional efficiency and adaptability.
Keywords:
AI in Antenna System Control, intelligent antenna monitoring systems, Signal Processing Techniques, Predictive Maintenance and Demand Forecasting, Integration of AI with Traditional Methods
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.