Artificial Intelligent Technology for Tunable Terahertz and Millimetre-wave Applications
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Submission Deadline: January 31, 2025
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The millimetre-wave, sub-terahertz (THz) and THz frequency ranges, especially from 30 GHz to 10 THz, are under exponential development for various applications such as antennas, detectors, metamaterials, plasmonic antennas and tunable devices, and is one of the most promising for future wireless communications such as 6G systems. Such arrangements are assumed to be used in both terrestrial and non-terrestrial networks. For different applications, some specifications such as terabit-per second data rate and everywhere coverage, acceptable reliable communication, low latency with massive connectivity are common challenges. From another point of view, the problem of mm-wave and THz beamforming must also be considered by focusing on the conductive materials used. In modern communication environments, the antenna system generally consists of antenna arrays and reflective/ refracting surfaces. Research activities on these topics require advanced materials with good electrical properties (to create efficient devices) and a high degree of reconfigurability. These challenges motivate, for example, research into graphene or similar 2D materials.
Designing high-performance antennas with tunable structures in THz band communication is a vital role and is excessively challenging due to micrometer dimensions and dielectric materials. Therefore, to overcome these challenges of designing high-performance structures, and considering the high degrees of freedom due to the multidisciplinary aspects, advanced methodologies are required to achieve the desired results. Recently, artificial intelligence (AI) has demonstrated its effectiveness as a tool to facilitate the optimization process for the design and optimization of high-dimensional microwave devices.
Potential topics include but are not limited to the following:
- Antenna and Tunable structure design at THz/mm-wave frequency;
- Employing AI for designing and optimizing high-dimensional radiating elements;
- Artificial neural network modeling of microwave devices;
- Tunable structure design and optimization for 6G communication networks;
- Deep neural networks for designing tunable designs;
- Automated designs with the help of AI technology.
Keywords:
- Artificial intelligent (AI)
- Deep learning (DL)
- Machine learning (ML)
- Millimetre-wave (mm-wave)
- Next-generation communication networks
- Optimization
- Tunable
- Terahertz (THz)
Guest Editors:
Ladislau Matekovits
Politecnico di Torino, Italy
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Lida Kouhalvandi
Dogus University, Turkiye
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Karu P. Esselle
University of Technology Sydney, Australia
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Wenjie Fu
University of Electronic Science and Technology of China, China
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