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Future Special Issues

 

  1. Antennas and Propagation Aspects of In-Band Full Duplex Application

    Guest Co-Editors: Danilo Erricolo, Dejan Filipovic, Haneda Katsuyuki, and Zhijun Zhang
    Submission Deadline: Oct 31, 2020
    Publication Date: Aug 01, 2021

  2. Artificial Intelligence in Radio Propagation for Communications

    Guest Co-Editors: Ruisi He, Buon Kiong Lau, Claude Oestges, Katsuyuki Haneda, and Bo Liu
    Submission Deadline (Original): Mar 31, 2021
    Submission Deadline (Extended): Aug 31, 2021
    Final Decision: Feb. 28, 2022
    Publication Date: May 2022

  3. Artificial Intelligence: New Frontiers in Real‐Time Inverse Scattering and Electromagnetic Imaging

    Guest Co-Editors: Manuel Arrebola, Maokun Li, and Marco Salucci
    Submission Deadline (Original): Mar 31, 2021
    Submission Deadline (Extended): Aug 31, 2021
    Final Decision: Feb. 28, 2022
    Publication Date: May 2022

  4. Machine Learning in Antenna Design, Modeling, and Measurements

    Guest Co-Editors: Francesco Andriulli, Pai-Yen Chen, Danilo Erricolo, Jian-Ming Jin
    Submission Deadline (Original): Mar 31, 2021
    Submission Deadline (Extended): Aug 31, 2021
    Final Decision: Feb. 28, 2022
    Publication Date: May 2022

  5. Smart Electromagnetic Environment

    Guest Co-Editors: Fan Yang, Danilo Erricolo, and Andrea Massa
    Submission Deadline (Original): May 31, 2021
    Submission Deadline (Extended): Oct 17, 2021
    Final Decision: Feb. 28, 2022
    Publication Date: June 2022

  6. Low‐Cost Wide‐Angle Beam‐Scanning Antennas

    Guest Co-Editors: Steven Gao, Y. Jay Guo, Safieddin (Ali) Safavi‐Naeini, Wonbin Hong, and Xuexia Yang
    Submission Deadline (Original): Aug 31, 2021
    Submission Deadline (Extended): Nov 30, 2021
    Final Decision: May 31, 2022
    Publication Date: Aug. 2022

Details of each Special Issue are given below.


Special Issue on Antennas and Propagation Aspects of In-Band Full Duplex Application

The frequency spectrum is congested due to usage on the part of many communication systems and a few radar and military systems. Most communication systems are half-duplex and they use separate bands or time slots to transmit and receive signals. This leads to underutilization of available resources and inefficient flow of information between wireless systems. Physical separation between the transmitter and receiver, with electromagnetic shielding or attenuation material placed in-between, can lead to their simultaneous operation in time and frequency; however, additional required space is often not available.

On the other hand, in-band full-duplex systems that transmit and receive simultaneously in the same frequency band overcome some of these issues. These systems, also known as simultaneous transmit and receive (STAR) systems, have potential to increase spectral efficiency by either doubling the number of users or by doubling the communication channel capacity for each user in the same frequency interval allocated for half-duplex communications.

However, the implementation of in-band full-duplex systems is challenged by self-interference between the strong transmitted and the weak received signal at each transceiver device. The higher transmitted power and narrower communication channel the greater are the isolation requirements, in some cases more than 150 dB. Moreover, wideband self-interference cancellation is more challenging than narrowband designs, leaving significant rooms for research before modern wideband radio communications can take full advantage of in-band full-duplex radios.

The prevailing thought is that the solution to the self-interference challenge is the combination among several approaches including: (i) antenna design; (ii) analog cancellation, for example through RF frontend design as its wider dynamic range and sensitivity is of crucial importance ; (iii) digital cancellation; In the context of STAR systems with maximum utilization of resources, applying one of these approaches is not sufficient to achieve the level of self-interference cancellation that leads to a workable system.

Moreover, the antenna subsystem design with co-channel self-interference reduction is required to ensure the low noise amplifiers and other actives in the chain do not overload. In this special issue, we consider novel contributions to self-interference cancellation that are based on antenna subsystem design and allow for the first level of cancellation in in-band full-duplex systems. We also gather new experimental evidences and models that explain them, based on extensive field tests of antenna systems along with physical and mathematical modeling of self-interference channels.

The purpose of this special issue is to draw attention to the latest progress in the understanding, development, and in-field deployment of antenna systems for in-band full-duplex applications. Contributions are sought for, but not limited to the following:

  • Both single antenna configurations and multi-antenna systems across frequency spectrum of interest for current and future narrow-and wide-bandwidth RF systems.
  • Advancements in novel techniques to integrate antennas and non-reciprocal devices or beamformers, use of novel materials and symmetries for improved self-interference cancellation.
  • New configurations and techniques for in-band full-duplex phased arrays and switched beam antennas.
  • Novel techniques for improved self-interference cancellation relying on polarization, space or beam multiplexing with major advancement in theory, experimental tests or demonstration in practical application scenarios.
  • Measurements of in-band full-duplex antenna subsystems, tolerance analysis, multi-physics analysis and co-design, platform effects inclusive of design for immunity to the host structure, use of STAR for other purposes than communications, MIMO in-band full-duplex antennas.

Guest Co-Editors:

Danilo Erricolo
University of Illinois at Chicago, Chicago, IL, USA , This email address is being protected from spambots. You need JavaScript enabled to view it.;

Dejan Filipovic
University of Colorado at Boulder, Boulder, CO, USA, This email address is being protected from spambots. You need JavaScript enabled to view it.;

Haneda Katsuyuki
Aalto University, Finland, This email address is being protected from spambots. You need JavaScript enabled to view it.;

Zhijun Zhang
Tsinghua University, China, This email address is being protected from spambots. You need JavaScript enabled to view it..

Deadlines

Paper Submission: Oct. 31, 2020;
Publication Date: Aug. 2021.


Artificial Intelligence in Radio Propagation for Communications

Recently, the rapidly growing wave of wireless data is pushing against the boundary of wireless communication system’s performance. Such pervasive and exponentially increasing data present imminent challenges and future wireless communications will require robust intelligent algorithms for different services in different scenarios. In such an era of big data where data mining and data analysis technologies are effective approaches for system evaluation and design, the applications of artificial intelligence (AI) in wireless communications are receiving a lot of attention. AI provides new and innovative solutions for the complex problem of communication system design. It is a powerful tool and a popular research topic with many potential applications to enhance wireless communications.

The field of radio propagation, which is important for the design and performance analysis of any wireless communication system, also benefits in this era. For example, clustering algorithms, as a subfield of AI, are widely used for propagation channel feature extraction, and the resulting cluster-based propagation channel models are popular in both academic and industry.

New learning-based approaches for radio channel prediction, which usually employs artificial neural networks or deep learning algorithms, are also receiving a lot of attention in communication system design and performance evaluation. More data mining techniques have been used for analyzing radio propagation data such as expectation maximization and support vector machines.

The focus of this Special Issue is to showcase a unified vision for the applications of AI in radio propagation for communications, or other relevant aspects. More specifically, the initial announcement encouraged emphasis in, but not limited to, the following areas:

  • Novel AI or AI-enabled techniques for radio propagation characterization and analysis.
  • AI-enabled data analysis and parameter extractions of wireless channels.
  • Clustering analysis for radio channel characterization and modeling.
  • AI-enabled channel modeling and communication system simulation.
  • AI algorithm design in radio propagation for the applications in communications.

Guest Co-Editors:

Ruisi He
Beijing Jiaotong University, China, This email address is being protected from spambots. You need JavaScript enabled to view it.

Buon Kiong Lau
Lund University, Sweden, This email address is being protected from spambots. You need JavaScript enabled to view it.

Claude Oestges
Universite Catholique de Louvain, Belgium, This email address is being protected from spambots. You need JavaScript enabled to view it.

Katsuyuki Haneda
Aalto University, Finland, This email address is being protected from spambots. You need JavaScript enabled to view it.

Bo Liu
University of Glasgow, UK, This email address is being protected from spambots. You need JavaScript enabled to view it.

Deadlines

 

 

Paper Submission Deadline (Original): Mar. 31, 2021
Paper Submission Deadline (Extended): Aug. 31, 2021
Final Decision: Feb. 28, 2022
Publication Date: May 2022


Artificial Intelligence: New Frontiers in Real‐Time Inverse Scattering and Electromagnetic Imaging

Understanding and solving complex problems in the physical world has been an intelligent endeavour of humankind. Moreover, the study of artificial intelligence embodies the dream of designing machines like humans. Research in deep learning (DL) techniques has attracted much attention in many application areas.

With the help of big data technology, massive parallel computing, and fast optimization algorithms, DL has greatly improved the performance of many problems in the speech and image research, power transportation networks or bio‐electromagnetics, among others.

Nowadays, DL is rapidly emerging in the antennas and propagation community as an extremely powerful paradigm for solving high‐complexity electromagnetic inverse scattering (IS) and imaging problems with unprecedented computational efficiency without reducing the accuracy and therefore reliability.

As a matter of fact, DL is a promising solution to achieve accurate pixel‐wise reconstructions with real‐time estimation performance, a desirable feature in many applications such as biomedical imaging, works of art and archaeological inspection, industrial non destructive testing and evaluation, trough‐the‐wall imaging, and subsurface imaging. With the spreading of DL techniques, improvement in learning capacity may allow machines to "learn" from a large amount of physical data and "master" the physical laws in certain controlled boundary conditions.

In the long run, a hybridization of fundamental physical principles with "knowledge" from big data could unleash numerous engineering applications that used to be impossible due to the limit of data information and ability of computation. As a result, more advanced IS and electromagnetic imaging techniques can be developed with improved accuracy, robustness, and computational efficiency.

The objective of this Special Issue is to report recent advancements in theory and applications of artificial intelligence and DL to solve electromagnetic IS and imaging problems within the research scope of Antennas and Propagation with extremely fast but reliable techniques. With this Special Issue, we hope to bring more attention and research efforts in our society to this emerging multi‐disciplinary field, resulting in an evolution of the state of the art.

Guest Co-Editors:

Manuel Arrebola
Universidad de Oviedo, Spain, E‐mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Maokun Li
Tsinghua University, E‐mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Marco Salucci
University of Trento, E‐mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Deadlines

 

 

Paper Submission Deadline (Original): Mar. 31, 2021
Paper Submission Deadline (Extended): Aug. 31, 2021
Final Decision: Feb. 28, 2022
Publication Date: May 2022


Machine Learning in Antenna Design, Modeling, and Measurements

Machine learning (ML) is the study of computational methods for improving performance by mechanizing the acquisition of knowledge from experience. In general, ML aims to provide increasing levels of automation in the knowledge engineering process, replacing much time-consuming human activity with automatic techniques that improve accuracy or efficiency by discovering and exploiting regularities in training data.

Nowadays, a broader family of ML techniques based on artificial neural networks have been developed. Examples include Deep Neural Network (DNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Generative Adversarial Network (GAN), and Deep Reinforcement Learning (DRL), which have been successfully applied to different engineering and science domains. Perhaps, applying machine learning to real-world electromagnetic problems is one of emerging trends in ML and artificial intelligence (AI).

Some recent successes include the accelerated design and measurement of antennas and artificial electromagnetic media (e.g., metamaterials and metasurfaces), as well as reinforcement strategies for computational electromagnetics (CEM). One may envision that fusion of ML algorithms, measurement data and physical models (e.g., CEM methods) will pave the way for building new generations of antennas, radars, CEM solvers, telemetering and remote sensing platforms.

The aim of this special issue is to showcase recent developments, advances, and new frontiers in the framework of ML algorithms and their potential applications to antennas and propagation problems, as well as to increase their visibility within the electromagnetics community.

This special issue will consider the latest in research in, but not limited to, the following areas:

  • Theoretical advances on ML algorithms specified for antennas and electromagnetic applications. Particularly, authors are encouraged to apply ML to address key EM-related challenges in smart antennas and arrays for autonomous vehicles, internet-of-things, MIMO, 5G networks and beyond.
  • Antenna pattern measurement, electromagnetic signature analysis, EM-based diagnosis and detection, and uncertainty quantification using ML algorithms.
  • Geometric and topological optimization and designs of elementary and arrayed radiation apertures, metamaterials and metasurfaces, and other electromagnetic devices.
  • Novel real-life data-driven applications to antennas, radars, remote sensing, telemetering, wave propagation, and electromagnetic engineering.
  • Artificial intelligence for Computational Electromagnetics including data-driven ML for EM modeling and simulation, fast data generation and reduced-order modeling for ML, AI-accelerated or AI-based solvers for CEM, and advanced antenna design optimization tools.

Guest Co-Editors:

Francesco Andriulli
Politecnico di Torino, This email address is being protected from spambots. You need JavaScript enabled to view it.

Pai-Yen Chen
University of Illinois at Chicago, This email address is being protected from spambots. You need JavaScript enabled to view it.

Danilo Erricolo
University of Illinois at Chicago, This email address is being protected from spambots. You need JavaScript enabled to view it.

Jian-Ming Jin
University of Illinois at Urbana-Champaign, This email address is being protected from spambots. You need JavaScript enabled to view it.

Deadlines

 

Paper Submission Deadline (Original): Mar. 31, 2021
Paper Submission Deadline (Extended): Aug. 31, 2021
Final Decision: Feb. 28, 2022
Publication Date: May 2022


Special Issue on Smart Electromagnetic Environment

The exponential growth of mobile data traffic in the last decades is expected to further increase in the next years, while all users are waiting to experience multi‐gigabit‐per‐second connections at any time.

Wireless infrastructures for future generation of mobile communication systems are required to guarantee unprecedented link performance levels, while minimizing the complexity, the power consumption and the cost of the architecture. Therefore, alternative solutions to the approach “more information and data through more power and more emissions of electromagnetic waves” are mandatory because of the existing electromagnetic congestion.

One solution is to look at the environment where propagation occurs, together with the wireless infrastructure and the users, as a whole system and try to improve the performance of the system by going beyond the standard concepts of wireless infrastructure and wireless channel to implement a “smart electromagnetic environment”.

In fact, while traditional communication systems focus the radiated power along the terminal direction to maximize link quality and information transfer by, for instance, increasing the antenna gain and reducing the sidelobe level (SLL), next generation multi‐user multi‐antenna architectures could maximize the signal-to-noise-ratio by spatially distributing the power to constructively exploit the wave scattering phenomena in the multi‐path propagation environment, regardless of the gain, the SLL, or the grating lobes.

As an example, for propagation in urban environments, the scattering scenario needs to be considered as an asset rather than an impediment. Accordingly, building walls may be seen as an opportunity to install smart reflectors to improve coverage at locations that cannot be reached through direct line-of-sight-paths.

It should be obvious that the implementation of the smart electromagnetic environment needs suitable processing tools and techniques allowing for the mandatory environment/infrastructure reconfigurability.

The purpose of this special issue is to draw attention to the implementation of the smart electromagnetic environment, i.e., a holistic approach where the traditional wireless infrastructure and buildings are designed to enhance electromagnetic propagation and quality of service for users. Contributions are sought for, but not limited to the following:

  • innovative theories and approaches (e.g., capacity-driven) for the design of wireless infrastructures;
  • new methods and advanced implementations of ‘smart skin’ for field manipulation;
  • innovative signal-processing (e.g., compressive-processing) techniques for sensing and communication signals;
  • opportunistic methods and solutions for the EM propagation improvements and increased coverage;
  • environmental-friendly antenna solutions and intelligent reflecting surfaces for next generation wireless communications;
  • machine learning and inverse design techniques for on-demand real-time wireless communications;
  • new applications of materials and new materials for smart EM environments.

Guest Co-Editors:

Fan Yang
Tsinghua University, Beijing, China, This email address is being protected from spambots. You need JavaScript enabled to view it.

Danilo Erricolo
University of Illinois Chicago, Chicago, IL, USA, This email address is being protected from spambots. You need JavaScript enabled to view it.

Andrea Massa
University of Trento, Trento, Italy, This email address is being protected from spambots. You need JavaScript enabled to view it.

Deadlines

Paper Submission Deadline (Original): May 31, 2021
Paper Submission Deadline (Extended): Oct 17, 2021
Final Decision: Feb. 28, 2022
Publication Date: June 2022


Special Issue on Low-Cost Wide-Angle Beam-Scanning Antennas

One of the key requirements in modern wireless systems is beam-scanning antennas. The traditional method is to employ mechanically beam-scanning reflectors which are, however, bulky, heavy, and have slow speed of beam scanning. Other important limitations of mechanical scanners is their lack of multi-beam scan capability and ability to conform with non-planar structures (conformal geometries), which are essential in a number of emerging systems requiring very low-profile structures.

An alternative technique is to employ electronically beam-scanning antennas using passive or active phased arrays. Main disadvantages of phased arrays are high complexity, high power consumption and high cost due to a large number of radio frequency (RF) or microwave phase shifters and T/R modules required.

The problems get worse for phased arrays at millimeter-wave, sub-THz and THz frequencies, due to significant losses in phase shifters and feed networks at higher frequencies. The digital beam-forming approach is even more costly and energy hungry due to the employment of a large number of RF modules and digital devices.

For civilian applications, it is critical to develop low-cost beam-scanning antenna technologies. There are significant interests from the industries (terrestrial, maritime, and space) and academics in investigating innovative development of low-cost beam-scanning antennas worldwide. Low-cost beam-scanning antennas are very promising for a wide range of applications such as the base stations and mobile phones of 5G/B5G/6G, mobile terminals for satellite communications on the move, automotive radars, imagers, small satellites data downlink, small satellites inter-satellite links, Internet of Things, Internet of Space, etc.

For example, Starlink, initiated by SpaceX, aims to deliver high speed broadband internet and global coverage from space. One of key challenges for Starlink is the high cost of beam-scanning antennas for user terminals on the ground. To ensure the success of Starlink project, SpaceX will have to find a way of developing the user terminal antennas at very low cost.

The purpose of this special issue is to draw attention to the latest progress in the theory, design, development, and in-field deployment of low-cost beam-scanning antennas for applications in base stations of mobile communications networks, mobile phones, mobile terminals for satellite communications on the move, radars, small satellites (Cube-Sat, Micro-Sat, Mini-Sat, Nano-Sat, Pico- Sat), Internet of Things, etc. Contributions are sought for, but not limited to the following:

  • Novel theory or techniques of designing low-cost wide-angle compact-size beam-scanning antennas at microwave, millimeter-wave, sub-THz or THz frequencies;
  • New configurations and techniques for wide-angle beam-scanning phased array antennas with low complexity;
  • Novel devices (phase shifters, etc.) or materials (artificial materials, functional materials, etc.) and their applications into practical implementation of low-cost power-efficient beam-scanning antenna systems;
  • Beam-forming algorithms and their application into practical implementation of low-cost power-efficient beam-scanning antenna systems;
  • Intelligent electromagnetic surfaces and their applications in practical implementation of beam-scanning compact-size antenna systems;
  • New configurations and techniques for high-gain multi-beam antennas or high-gain beam-switched antennas;
  • Manufacturing technologies for low-cost beam-scanning antennas;
  • Multi-physics analysis and co-design of beam-scanning antenna sub-system and other subsystems (e.g. thermal sub-system, mechanical structures, power sub-system, etc.) or platforms;
  • Measurement and calibration techniques, particularly on-line (integrated or internal) calibration/characterization schemes, for beam-scanning antennas.

Guest Co-Editors:

Steven Gao
University of Kent, UK, This email address is being protected from spambots. You need JavaScript enabled to view it.

Y. Jay Guo
University of Technology, Australia, This email address is being protected from spambots. You need JavaScript enabled to view it.

Safieddin (Ali) Safavi-Naeini
University of Waterloo, Canada, This email address is being protected from spambots. You need JavaScript enabled to view it.

Wonbin Hong
Pohang University of Science and Technology, South Korea, This email address is being protected from spambots. You need JavaScript enabled to view it.

Xuexia Yang
Shanghai University, This email address is being protected from spambots. You need JavaScript enabled to view it.

Deadlines

Paper Submission Deadline (Original): Aug. 31, 2021
Paper Submission Deadline (Extended): Nov. 30, 2021
Final Decision: May 31, 2022
Publication Date: Aug. 2022