1-D broadside-radiating leaky-wave antenna based on a numerically synthesized impedance surface

A newly-developed deterministic numerical technique for the automated design of metasurface antennas is applied here for the first time to the design of a 1-D printed Leaky-Wave Antenna (LWA) for broadside radiation. The surface impedance synthesis process does not require any a priori knowledge on the impedance pattern, and starts from a mask constraint on the desired far-field and practical bounds on the unit cell impedance values. The designed reactance surface for broadside radiation exhibits a non conventional patterning; this highlights the merit of using an automated design process for a design well known to be challenging for analytical methods. The antenna is physically implemented with an array of metal strips with varying gap widths and simulation results show very good agreement with the predicted performance

Beam scanning by liquid-crystal biasing in a modified SIW structure

A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

On the Road to 6G: Visions, Requirements, Key Technologies and Testbeds

Fifth generation (5G) mobile communication systems have entered the stage of commercial development, providing users with new services and improved user experiences as well as offering a host of novel opportunities to various industries. However, 5G still faces many challenges. To address these challenges, international industrial, academic, and standards organizations have commenced research on sixth generation (6G) wireless communication systems. A series of white papers and survey papers have been published, which aim to define 6G in terms of requirements, application scenarios, key technologies, etc. Although ITU-R has been working on the 6G vision and it is expected to reach a consensus on what 6G will be by mid-2023, the related global discussions are still wide open and the existing literature has identified numerous open issues. This paper first provides a comprehensive portrayal of the 6G vision, technical requirements, and application scenarios, covering the current common understanding of 6G. Then, a critical appraisal of the 6G network architecture and key technologies is presented. Furthermore, existing testbeds and advanced 6G verification platforms are detailed for the first time. In addition, future research directions and open challenges are identified for stimulating the on-going global debate. Finally, lessons learned to date concerning 6G networks are discussed

Microwave Sensing and Imaging

In recent years, microwave sensing and imaging have acquired an ever-growing importance in several applicative fields, such as non-destructive evaluations in industry and civil engineering, subsurface prospection, security, and biomedical imaging. Indeed, microwave techniques allow, in principle, for information to be obtained directly regarding the physical parameters of the inspected targets (dielectric properties, shape, etc.) by using safe electromagnetic radiations and cost-effective systems. Consequently, a great deal of research activity has recently been devoted to the development of efficient/reliable measurement systems, which are effective data processing algorithms that can be used to solve the underlying electromagnetic inverse scattering problem, and efficient forward solvers to model electromagnetic interactions. Within this framework, this Special Issue aims to provide some insights into recent microwave sensing and imaging systems and techniques

Kablosuz sensör ağlarinda yönlü antenlerle enerji̇ veri̇mli̇ yönlendi̇rme

Without measurements, sustainable development effort can not progress in the right direction. Wireless sensor networks are vital for monitoring in real time and making accurate measurements for such an endeavor. However small energy storage in the sensors can become a bottleneck if the wireless sensor network is not optimized at the hardware and software level. Directional antennas are such optimization technologies at the hardware level. They have advantages over the omnidirectional antennas, such as high gain, less interference, longer transmission range, and less power consumption. In wireless sensor networks, most of the energy is consumed for communication. Considering the limited energy in small scale batteries of the sensors, energy efficient (aware) routing, is one of the most important software optimization techniques. The main goal of the technique is to improve the lifetime of the wireless sensor networks. In the light of these observations, it is desirable to do a coupled design of directional antennas with network software, for fully exploiting the advantages offered by directional antenna technology. In this thesis, the possibilities of doing such integrated design are surveyed and improvements are suggested. The design of the proposed microstrip patch antenna array is discussed and the performance characteristics are assessed through simulations. In the benchmarks, the proposed routing method showed improvements in energy usage compared to the existing approaches.Ölçümler olmadan sürdürülebilir kalkınma çabaları doğru yönde ilerleyemez. Bu tür çabalar için, kablosuz sensör ağları, gerçek zamanlı olarak izleme ve kesin ölçümler yapmak için vazgeçilemez unsurdur. Ancak, sensör ağı, donanım ve yazılım düzeylerinde optimize edilmemişse, sensörlerde enerji yetersizliği görülebilinir. Yönlü antenler, donanım düzeyinde uygulanan optimizasyon teknolojilerinden biri olmakla birlikte, çok yönlü antenlerden farklı olarak, yüksek kazanç, daha az parazit, daha uzun iletim mesafesi ve daha az güç tüketimi sağlarlar. Kablosuz sensör ağlarında enerjinin çoğu iletişim için tüketilir. Sensörlerdeki limitli enerjili küçük ölçekli piller göz önüne alındığında, yazılım düzeyindeki önemli metodlardan biri olan enerji verimli (duyarlı) yönlendirme protokolü, kablosuz sensör ağının genel enerji kullanımını optimize etmek ve ömrünü uzatmak için gereklidir. Bu gözlemlerin ışığında, yönlü anten teknolojisinin sunduğu potansiyel avantajlardan tam olarak yararlanmak için, yönlü antenlerin ağ yazılımıyla birlikte entegre tasarımını yapmak arzu edilir. Bu tezde, böyle bir entegre tasarımın yapılma olasılıkları araştırılmış ve iyileştirmeler önerilmiştir. Tezde, küçük şeritli yamalı anten dizisinin tasarımı tartışılmış ve performans karakteristikleri simulasyonlarla ölçülmüştür. Önerilen yönlendirme algoritması, diğer yönlendirme algoritmaları ile karşılaştırıldığında, enerji kullanımında iyileştirmeler göstermiştirM.S. - Master of Scienc

Remote Sensing

This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas

A Survey on Model-based, Heuristic, and Machine Learning Optimization Approaches in RIS-aided Wireless Networks

Reconfigurable intelligent surfaces (RISs) have received considerable attention as a key enabler for envisioned 6G networks, for the purpose of improving the network capacity, coverage, efficiency, and security with low energy consumption and low hardware cost. However, integrating RISs into the existing infrastructure greatly increases the network management complexity, especially for controlling a significant number of RIS elements. To unleash the full potential of RISs, efficient optimization approaches are of great importance. This work provides a comprehensive survey on optimization techniques for RIS-aided wireless communications, including model-based, heuristic, and machine learning (ML) algorithms. In particular, we first summarize the problem formulations in the literature with diverse objectives and constraints, e.g., sum-rate maximization, power minimization, and imperfect channel state information constraints. Then, we introduce model-based algorithms that have been used in the literature, such as alternating optimization, the majorization-minimization method, and successive convex approximation. Next, heuristic optimization is discussed, which applies heuristic rules for obtaining low-complexity solutions. Moreover, we present state-of-the-art ML algorithms and applications towards RISs, i.e., supervised and unsupervised learning, reinforcement learning, federated learning, graph learning, transfer learning, and hierarchical learning-based approaches. Model-based, heuristic, and ML approaches are compared in terms of stability, robustness, optimality and so on, providing a systematic understanding of these techniques. Finally, we highlight RIS-aided applications towards 6G networks and identify future challenges.Comment: This paper has been accepted by IEEE Communications Surveys and Tutorial

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts