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A planar array of coupled split-ring resonators (SRRs) shows many resonances over a broad frequency band at which the structure efficiently emits waves. The mode splitting due to coupling offers an unprecedented way for configuring the radiation pattern of the SRR array. It is shown that specific radiation patterns that include dipolar and quadrupolar oscillation patterns can be constructed by varying the self-impedance of the resonators. Presented is an experimental demonstration based on this concept using electronically tuned SRRs. With this setup, the radiation pattern at a given frequency can be adjusted using a DC voltage command. The proposed structure is an efficient and compact alternative to traditional electronic beam-steering antennas.

\"Wireless Multi-Antenna Channels: Modeling and Simulation focuses on modeling and simulation of multiple antennas channels, including multiple input multiple output (MIMO) communication channels and impact of such models on channel estimation and system performance. Both narrowband and wideband models are discussed. The book covers topics related to modeling of MIMO channel, their numerical simulation, estimation and prediction, as well as applications to receive diversity and space-time coding techniques. Contains significant fundamental/background material, as well as novel research coverage, which make the book suitable for both graduate students and researchers Addresses issues such as key-hole, correlated and non i.i.d. channels in the frame of the Generalized Gaussian approach Provides a unique treatment of generalized Gaussian channels and orthogonal channel representation Reviews different interpretations of scattering environment, including geometrical models Focuses on the analytical techniques which give a good insight into the design of systems on higher levels Describes a number of numerical simulators demonstrating the practical use of this material. Accompanying website will contain additional materials and practical examples for self-study\"--

An important element of modern telecommunications is wireless radio networks, which enable mobile subscribers to access wireless networks. The cell area is divided into independent sectors served by directional antennas. As the number of mobile network subscribers served by a single base station increases, the problem of interference related to the operation of the radio link increases. To minimize the disadvantages of omnidirectional antennas, base stations use antennas with directional radiation characteristics. This solution allows you to optimize the operating conditions of the mobile network in terms of reducing the impact of interference, better managing the frequency spectrum and improving the energy efficiency of the system. The work presents an adaptive antenna algorithm used in mobile telephony. The principle of operation of adaptive systems, the properties of their elements and the configurations in which they are used in practice are described. On this basis, an algorithm for controlling the radiation characteristics of adaptive antennas is presented. The control is carried out using a microprocessor system. The simulation model is described. An algorithm was developed based on the Mathcad mathematical program, and the simulation results of this algorithm, i.e., changes in radiation characteristics as a result of changing the mobile position of subscribers, were presented in the form of selected radiation characteristics charts.

Long Range Wide Area Network (LoRaWAN) has become an attractive option for maritime communication because it is low-cost, long-range, and energy-efficient. Yet its performance at sea is often limited by fading, interference, and the strict energy budgets of maritime Internet of Things (IoT) devices. This review, prepared in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, examines 23 peer-reviewed studies published between 2019 and 2025 that explore adaptive antenna solutions for LoRaWAN in marine environments. The work covered four main categories: switched-beam, phased array, reconfigurable, and Artificial Intelligence or Machine Learning (AI/ML)-enabled antennas. Results across studies show that adaptive approaches improve gain, beam agility, and signal reliability even under unstable conditions. Switched-beam antennas dominate the literature (45%), followed by phased arrays (30%), reconfigurable designs (20%), and AI/ML-enabled systems (5%). Unlike previous reviews, this study emphasizes maritime propagation, environmental resilience, and energy use. Despite encouraging results in signal-to-noise ratio (SNR), packet delivery, and coverage range, clear gaps remain in protocol-level integration, lightweight AI for constrained nodes, and large-scale trials at sea. Research on reconfigurable intelligent surfaces (RIS) in maritime environments remains limited. However, these technologies could play an important role in enhancing spectral efficiency, coverage, and the scalability of maritime IoT networks.

Consider a decode‐and‐forward wireless relay system assisted by an intelligent reflection surface (IRS), where a robust design on receive beamforming at the relay and reflection coefficients at the IRS is studied. The worst‐case signal‐to‐noise ratio maximization problem is formulated, subject to the reflection coefficients (with either continuous or discrete phases) constraints, under the assumption of imperfect channel state information for all channels. To cope with the hard problem, an equivalent nonconvex quadratic optimization problem with a simpler form is derived, and then the Cauchy‐Schwarz inequality is applied to update the beamforming and a cyclic process is proposed to update the reflection coefficients, where a closed‐form optimal solution is computed in each step. It turns out that the proposed algorithm achieves a locally optimal solution for the robust design problem. Simulation results show that the proposed robust design outperforms an existing non‐robust design and two robust designs via semidefinite relaxation technique. In a DF IRS‐aided wireless relay system, we design robust receive beamforming at the relay station and reflection coefficients at the IRS under the assumption of imperfect channel state information for all channels. We formulate an SNR (at the relay) maximization problem subject to unit‐modulus constraints on the reflection coefficients, and the problem is reformulated into a hard quadratic maximization problem and solved iteratively by the Cauchy‐Schwarz inequality to update the beamforming and by a cyclic process to search the optimal reflection coefficients, where a closed‐form optimal solution is computed in each step.

The book addresses the current demand for a scientific approach to advanced wireless technology and its future developments, including the current move from 4G to 5G wireless systems (2020), and the future to 6G wireless systems (2030). It gives a clear and in-depth presentation of both antennas and the adaptive signal processing that makes antennas powerful, maneuverable, and necessary for advanced wireless technology. Moving towards the increasing demand for a scientific approach to smart antennas, the book presents electromagnetic signal processing techniques to both control the antenna beam and to track the moving station, which is required for effective, fast, dynamic beamforming. In addition to presenting new, memory efficient and fast algorithms for smart antennas, another helpful feature of the book is the inclusion of complete listings of MATLABTM codes for powerful techniques such as Artificial Intelligence (AI) beamforming, Analytical Phase Shift technique and the traditional Least Mean Square method, The student, researcher or engineer may readily use these codes to gain confidence in understanding, as well as to develop and deploy powerful, new smart antenna techniques. The first part of the book presents a comprehensive description and analysis of basic antenna theory, starting from short dipole antennas to array antennas. This section also includes important concepts related to antenna parameters, electromagnetic wave propagation, the Friis equation, the radar equation and wave reflection and transmission through media. The second part of the book focuses on smart antennas, commencing from a look at traditional approach to beam forming before getting into the details of smart antennas. Complete derivation and description of the techniques for electromagnetic field signal processing techniques for adaptive beam forming are presented. Many new and research ideas are included in this section. A novel method for fast, low memory and accurate, maneuverable single beam generation is presented, as well as other methods for beamforming with fewer elements with a simple method for tracking the mobile antenna and station. In this section, for completeness, the use of antenna signal processing for synthetic aperture techniques for imaging are also presented, specifically the Inverse Synthetic Aperture Imaging technique. Some computer codes are given for the student and researcher to get started with new areas to explore. The third part of the book presents technological aspects of advanced wireless technology, including Artificial Intelligence driven steerable single beams, the 5G wireless system and the various devices needed to construct the system. While the books' main emphasis is theoretical understanding and design with the basic tools needed to develop powerful computer code for the smart antennas, it also provides the algorithms or codes in a number of important cases to show how the smart antenna computer codes may be developed using electromagnetic signal processing. Artificial Intelligence (AI) driven beam forming is presented using computationally fast and low-memory demanding technique for AI beam forming is presented with the different excitation functions available. The final chapter outlines certain techniques to develop smart antenna algorithms and computer codes for beginners, researchers, and engineers, and furthermore, to implement a part of what was learnt, including AI techniques.

This book covers all areas of smart antennas, electromagnetic interference, and microwave antennas for wireless communications. Smart antennas or adaptive antennas are multi-antenna components on one or both sides of a radio communication connection, combined with advanced signal processing algorithms. They've evolved into a critical technology for third-generation and beyond mobile communication systems to meet their lofty capacity and performance targets. It seems that a significant capacity gain is achievable, particularly if they are employed on both sides of the connection. There are several essential characteristics of these systems that need scientific and technical investigation. Included in the book are beamforming, massive MIMO, network MIMO, mmwave transmission, compressive sensing, MIMO radar, sensor networks, vehicle-to-vehicle communication, location, and machine learning.

Beam forming processing of adaptive antenna introduces distortions of group delay. For high-precision GNSS applications, the distortions cannot be ignored. In order to solve the problem, an evaluating method of adaptive array group delay based on availability beam is proposed. This method consists of three steps: firstly, setting the available beamwidth of the antenna; secondly, configuring different interference scenarios and calculating the group delay variation within the available beamwidth for each scenario; finally, averaging group delay variations obtained across different scenarios to derive the mean group delay variation. This method is applied to evaluate the group delay performance of four typical planar arrays under interference conditions. Experimental results indicate that the uniform circular array is the optimal high-precision adaptive antenna configuration under interference conditions. Additionally, by setting an appropriate available beam threshold, the stability of the average group delay of the adaptive antenna can be improved. The method can quickly and effectively evaluate the group delay of the adaptive antenna, and the evaluation results can serve as a reference for the performance assessment of high-precision GNSS adaptive antennas.