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Since the launch of Second-Generation Networks (2G), planning for each future mobile service was initiated many years before its commercial launch. In 2019, 5G Networks begun to be deployed commercially after almost ten years of planning. Similarly, the race for the 6G wireless networks that will be operational in 2030 has already started. To fulfill its potential in the upcoming decade, 6G will undoubtedly require an architectural orchestration based on the amalgamation of existing solutions and innovative technologies. The book will begin by evaluating the state of the art of all current mobile generations' while looking into their core building blocks. 6G implementation will require fundamental support from Artificial Intelligence (AI) and Machine Learning on the network's edge and core, including a new Radio Frequency (RF) spectrum. The 6G use cases will require advanced techniques for enabling the future wireless network to be human-centric, ensuring enhanced quality of experience (QoE) for most of its applications. The concept of Human Bond Communication Beyond 2050 (Knowledge Home) and Communication, Navigation, Sensing, and Services (CONASENSE) will also profit from future wireless communication. Terahertz domains will exploit the ultra-Massive Multiple Input Multiple Output Antennas (UM-MIMO) technologies to support Terabits' data throughputs. Moreover, optical wireless communications (OWC) will also come into play to support indoor and outdoor high-data rates. Further expansion of 6G core entities will support the novel concept of Society 5.0. Quantum computing processing and communications is also likely to be added into the 6G ecosystem with security managed by blockchain orchestration for a robust network.

A novel specific emitter identification method based on transient communication signal's time–frequency–energy distribution obtained by Hilbert–Huang transform (HHT) is proposed. The transient starting point is detected using the phase-based method and the transient endpoint is detected using a self-adaptive threshold based on the HHT-based energy trajectory. Thirteen features that represent both overall and subtle transient characteristics are proposed to form a radio frequency (RF) fingerprint. The principal component analysis method is used to reduce the dimension of the feature vector and a support vector machine is used for classification. A signal acquisition system is designed to capture the signals from eight mobile phones to test the performance of the proposed method. Experimental results demonstrate that the method is effective and the proposed RF fingerprint can represent more subtle characteristics than the RF fingerprints based on instantaneous amplitude, phase, frequency and energy envelope. This method can be equally applicable for any wireless emitter to enhance the security of the wireless networks.

Microelectromechanical systems (MEMS) that are based on flexible substrates are widely used in flexible, reconfigurable radio frequency (RF) systems, such as RF MEMS switches, phase shifters, reconfigurable antennas, phased array antennas and resonators, etc. When attempting to accommodate flexible deformation with the movable structures of MEMS, flexible RF MEMS are far more difficult to structurally design and fabricate than rigid MEMS devices or other types of flexible electronics. In this review, we survey flexible RF MEMS with different functions, their flexible film materials and their fabrication process technologies. In addition, a fabrication process for reconfigurable three-dimensional (3D) RF devices based on mechanically guided assembly is introduced. The review is very helpful to understand the overall advances in flexible RF MEMS, and serves the purpose of providing a reference source for innovative researchers working in this field.

Alzheimer’s disease (AD) is a globally alarming neuro-degenerative abnormality for elderly people. The earliest afflicted regions in AD are the brain tissues that form memory and other cognitive functions. Mild Cognitive Impairment (MCI) is the stage of dementia between Cognitively Normal (CN) and AD. Late-MCI is the last stage of MCI, also denominated as the early-stage of AD (EAD). The classification of EAD is important for preventing or delaying the development of AD. Due to the complex molecular and physical changes in the brain, medical experts struggle to classify EAD. By extracting critical features from brain images, diseases such as EAD can be effectively classified. Deep Neural Network (DNN) is a widely used machine learning (ML) technique for feature extraction and classification. In this work, we propose a DNN model for optimal feature extraction. By taking VGG-19 as a reference model, we used the concept of a dense block to pass maximal features throughout the network. We combined min-max-pooling layers to preserve both maximum and minimum valued feature information. Moreover, we’ve replaced all convolution layers with Inception-block to preserve fewer but more diverse parameters. Next, we used the principal component analysis (PCA) approach to select only the best features. Finally, we used a Random Forest (RF) classifier to classify EAD, CN, and AD. It is observed that, with an average performance rate of 98.08%, the proposed technique had the highest classification performance in CN vs. EAD. The proposed method also convincingly outperforms all of the discussed state-of-the-art and other classifiers

This paper presents a 0.8 V low-power CMOS voltage-controlled oscillator (VCO) with a wide tuning range, fabricated using a TSMC 0.18 μm process. The proposed design incorporates body-biasing techniques and an optimized varactor structure to achieve a tuning range of 1124 MHz (5.829–4.705 GHz) and low phase noise of −117.6 dBc/Hz at a 1 MHz offset. Operating at an ultra-low supply voltage of 0.8 V, the VCO consumes only 3.4 mW, demonstrating excellent power efficiency. A buffer circuit is also employed to enhance output symmetry and suppress flicker noise without introducing additional control complexity. With a figure-of-merit (FOM) of −188.6 dBc/Hz and a wide tuning range of 22.2%, the proposed VCO is well-suited for modern low-power communication systems, including 802.11ac, 5G transceivers, satellite links, and compact IoT devices.

This research review article provides a detailed examination of SMA (SubMiniature version A) connectors, which are integral components in high-frequency electronic systems. Through extensive S-parameter and time-domain reflectometry (TDR) measurements conducted on various SMA connector constructions, this study aims to evaluate the performance and impact of SMA connectors on signal integrity. Results reveal insights into the comparative performance of different SMA connector types mounted on PCB land pads, highlighting their strengths and limitations. Additionally, this paper explores the application of reference plane cut-outs for discontinuity impedance compensation, aiming to enhance the frequency response of SMA connectors. By linking measured performance parameters with relative market prices, this study offers valuable insights into the economic viability of different SMA connector types. The best and worst performing SMA connector measurements reveal an S11 < −10 dB bandwidth of more than 8 GHz and 1.5 GHz and a transition impedance of 46.5 Ω and 21 Ω, respectively. Overall, this research contributes to advancing the understanding and selection of SMA connectors for RF applications in telecommunications, aerospace, medical devices, and beyond.

Electrical connectors play a vital role in ensuring reliable signal transmission in high-frequency microsystems. This study explores the impact of microscale scratch-induced surface roughness on the alternating current (AC) contact impedance of RF coaxial connectors. Unlike traditional approaches that assume idealized surface conditions, controlled micro-defects were introduced at the central contact interface to establish a quantitative relationship between surface morphology and signal degradation. An equivalent circuit model was constructed to account for local impedance variations and the cumulative effects of cascaded connector interfaces. The model was validated using S-parameter measurements obtained from vector network analyzer (VNA) testing, showing strong agreement with simulation results. Experimental results reveal that the low-roughness (0.4 μm) contact surfaces lead to degraded signal integrity due to insufficient micro-contact formation. In contrast, scratch-induced moderate roughness (0.8–4.8 μm) improves transmission performance, although signal quality declines as roughness increases within this range. These effects are further amplified in multi-connector configurations due to accumulated impedance mismatches. This work provides new insight into the coupling between microscale surface features and frequency-domain transmission characteristics, offering practical guidance for surface engineering, contact design, and the development of miniaturized, high-reliability radio frequency interconnects for next-generation communication systems.

Multi-channel radio frequency (RF) signal generation, facilitated by photonic technology, offers significant potential for generating coherent signals with a high frequency and low phase noise, providing multifunctional capabilities across diverse platforms, including RF and photonic systems. Traditional methods for multi-channel photonic RF signal generation typically entail the integration of diverse optical components, such as filters and amplifiers. However, this integration often results in compromises related to power efficiency, cost-effectiveness, and implementation simplicity. To address these challenges, we propose a novel method for generating multi-channel photonic RF signals based on optical injection locking technology. This approach eliminates the necessity for traditional optical components, leading to a substantial enhancement in the performance of photonic RF signals. We present the design of an optical injection locking-based multi-channel photonic RF signal generation schematic and theoretically evaluate its Signal-to-Noise Ratio (SNR) and eye pattern performance for data modulation using the Lumerical INTERCONNECT simulator. Our results reveal a significant 1.3-dB and 3.6-dB enhancement in SNR for 30-GHz and 60-GHz signals, respectively. Furthermore, we observed an improved communication performance, as evidenced by enhanced eye patterns in 3-Gbps data transmission compared to passive photonic RF signal generation methods.

This book shows you how to develop a hybrid mm-wave chipless Radio Frequency Identification (RFID) system, which includes chip-less tag, reader hardware, and detection algorithm that use image processing and machine learning (ML) techniques. It provides the background and information you need to apply the concepts of AI into detection and chip-less tag signature printable on normal plastic substrates, instead of the conventional peak/nulls in the frequency tags. You’ll learn how to incorporate new AI detection techniques along with cloud computing to lower costs. You’ll also be shown a cost-effective means of image construction, which can lower detection errors. The book focuses on side-looking-aperture-radar (SLAR) with a combination of deep learning to provide a much safer means of chipless detection than the current iSAR technique, and includes extensive coverage of reader array antennas, beamforming, and MIMO. Each chapter includes practical examples of design. With its emphasis on mm-waveband and the practical side of design and engineering of the chipless tags, reader and detection algorithms, this is an excellent resource for industry engineers, design engineers and university researchers.