Search Results (1,408)

In this article, a chip-fed millimeter-wave high-gain antenna system with in-antenna power combining capability is presented. A low-profile resonant cavity antenna (RCA) array is fed by multiple spherical dielectric resonators (DRs), demonstrating its multi-feed capabilities. Each of the DRs is fed by two microstrip resonators on a planar circuit board. A printed superstrate is used in the proposed RCA as the partially reflecting superstrate (PRS), which makes the antenna profile small. To increase the directivity and gain, a 2 × 2 RCA array is developed. The demonstrated design shows a prominent peak gain of 25.03 dBi, a radiation efficiency of more than 80% and 3.38 GHz 3 db gain-bandwidth while maintaining a low profile. To steer the beam of the demonstrated 2 × 2 RCA array in a wide angular range with a low side-lobe-level, two planar all-dielectric passive beam steering metasurfaces have been designed and optimized. A detailed analysis of the optimization procedure is presented in this article. This numerical investigation is vitally important for realizing beam steering metasurfaces with suppressed side-lobe-level, wide bandwidth, excellent efficiency and less complexity. Full article

Optoelectronic oscillators (OEOs) have emerged as indispensable tools for generating low-phase-noise microwave and millimeter-wave signals, which are critical for a variety of high-performance applications. These include radar systems, satellite links, electronic warfare, and advanced instrumentation. The ability of OEOs to produce signals with exceptionally low phase noise makes them ideal for scenarios demanding high signal purity and stability. In radar systems, low-phase-noise signals enhance target detection accuracy and resolution, while, in communication networks, such signals enable higher data throughput and improved signal integrity over extended distances. Furthermore, OEOs play a pivotal role in precision instrumentation, where even minor noise can compromise the performance of sensitive equipment. This review examines the progress in OEO technology, transitioning from classical designs relying on long optical fiber delay lines to modern integrated systems that leverage photonic integration for compact, efficient, and tunable solutions. Key advancements, including classical setups, hybrid designs, and integrated configurations, are discussed, with a focus on their performance improvements in phase noise, side-mode suppression ratio (SMSR), and frequency tunability. A 20-GHz oscillation with an SMSR as high as 70 dB has been achieved using a classical dual-loop configuration. A 9.867-GHz frequency with a phase noise of −142.5 dBc/Hz @ 10 kHz offset has also been generated in a parity–time-symmetric OEO. Additionally, integrated OEOs based on silicon photonic microring resonators have achieved an ultra-wideband tunable frequency from 3 GHz to 42.5 GHz, with phase noise as low as −93 dBc/Hz at a 10 kHz offset. The challenges in achieving fully integrated OEOs, particularly concerning the stability and phase noise at higher frequencies, are also explored. This paper provides a comprehensive overview of the state of the art in OEO technology, highlighting future directions and potential applications. Full article

This study conducted a classification analysis of hydrometeor types during a typical stratiform mixed cloud precipitation event in the rainy season using data from the Liupan Mountains micro rain radar power spectra. The primary research findings are as follows: (1) Utilizing the RaProM method synthesizes the information of particle falling velocity, equivalent radar reflection coefficient, particle scale characteristics at different stages, and the location of the bright zone in the zero-degree layer to classify hydrometeors during this precipitation process, and the results show that drizzle and raindrop distribution time periods do not match with the raindrop spectra and rain intensities observed by the DSG5 ground-based precipitation gauge. (2) Sensitivity experiments conducted on the RaProM method revealed that after modifying the discrimination thresholds for drizzle and raindrops, the distributions of drizzle and raindrops were more aligned with ground-based raindrop spectrum observations. Furthermore, these adjustments also showed better consistency with the radar reflectivity factor, Doppler velocity, and velocity spectrum width thresholds used by existing millimeter-wave cloud radars to discriminate between drizzle and raindrops. (3) Various kinds of hydrometeors show different vertical distribution characteristics in three precipitation stages: weak, strong, and weak. In the two weak precipitation stages, hydrometeors mainly existed in the form of snowflakes at altitudes above the zero-degree layer and in the form of drizzle at altitudes below the zero-degree layer. The vertical distribution disparity of hydrometeors between the mountain peak and base sites demonstrates that terrain significantly influences hydrometeors during the precipitation process. Full article

This work addresses a critical challenge in microscale computational electromagnetics for liquid crystal-based reconfigurable components: the inadequate capability of current software to accurately identify and simulate higher-order modes (HoMs) in complex electromagnetic structures. Specifically, commercial simulators often fail to capture modes such as Transverse Electric (TE11) beyond the fundamental transverse electromagnetic (TEM) mode in coaxial liquid crystal phase shifters operating in the terahertz (THz) regime, leading to inaccurate performance predictions and suboptimal designs for telecommunication engineering applications. To address this limitation, we propose a novel diagnostic methodology incorporating three lossless assumptions to enhance the identification and analysis of pseudo-HoMs in full-wave simulators. Our approach theoretically eliminates losses associated with metallic conductivity, dielectric dissipation, and reflection effects, enabling precise assessment of frequency-dependent HoM power propagation alongside the primary TEM mode. We validate the methodology by applying it to a coaxially filled liquid crystal variable phase shifter device structure, underscoring its effectiveness in advancing the design and characterization of THz devices. This work provides valuable insights for researchers and engineers utilizing closed-source commercial simulators in micro- and nano-electromagnetic device development. The findings are particularly relevant for microscale engineering applications, including millimeter-wave (mmW), sub-mmW, and THz systems, with potential impacts on next-generation communication technologies. Full article

This study evaluates different machine learning algorithms in detecting and identifying drones using radar data from a 60 GHz millimeter-wave sensor. These signals were collected from a bionic bird and two drones, namely DJI Mavic and DJI Phantom 3 Pro, which were represented in complex form to preserve amplitude and phase information. The first benchmarks used four algorithms, namely long short-term memory (LSTM), gated recurrent unit (GRU), one-dimensional convolutional neural network (Conv1D), and Transformer, and they were benchmarked for robustness under noisy conditions, including artificial noise types like white noise, Pareto noise, impulsive noise, and multipath interference. As expected, Transformer outperformed other algorithms in terms of accuracy, even on noisy data; however, in certain noise contexts, particularly Pareto noise, it showed weaknesses. For this purpose, we propose Multimodal Transformer, which incorporates more statistical features—skewness and kurtosis—in addition to amplitude and phase data. This resulted in a improvement in detection accuracy, even under difficult noise conditions. Our results demonstrate the importance of noise in processing radar signals and the benefits afforded by a multimodal presentation of data in detecting unmanned aerial vehicle and birds. This study sets up a benchmark for state-of-the-art machine learning methodologies for radar-based detection systems, providing valuable insight into methods of increasing the robustness of algorithms to environmental noise. Full article

In this article, we propose a cell-free network architecture for an unmanned aerial vehicle (UAV) base station (BS), i.e., UBS, incorporating high-altitude platform stations (HAPSs) as central processing units (CPUs). The goal is to guarantee the quality of service (QoS) of user equipment (UE), reduce energy consumption, extend communication time, and facilitate rescue operations. The millimeter-wave (mmWave) frequency band is deployed in access and backhaul links to satisfy UE QoS requirements and high backhaul demands. The proposed framework jointly optimizes user association, backhaul bandwidth allocation, and power allocation to maximize energy efficiency while meeting QoS requirements. The optimization problem, modeled as non-convex mixed-integer nonlinear fractional programming, is solved through a three-stage iterative algorithm. This includes (1) optimizing power allocation based on Dinkelbach transformation and a successive convex approximation (SCA) method, (2) clustering UBSs using the Lagrangian method, and (3) deriving a closed-form bandwidth allocation factor. The proposed algorithm significantly outperforms many traditional algorithms in performance while maintaining low computational complexity. Full article

This work reports the concept of a shunt negative feedback technique for implementing a millimeter-wave wideband low-noise amplifier. The proposed shunt negative feedback network consists of a resistor–capacitor–inductor configuration. The proposed feedback network can achieve simultaneous noise and input matching (SNIM) over a wide frequency range by adjusting the values of the resistor–capacitor–inductor configuration based on numerical analysis. By adopting the SNIM-based gain core as the first stage of the amplifier, the simulation results of the three-stage low-noise amplifier in a 150-nm GaAs pHEMT process achieve a gain of 15.6–18.6 dB and a noise figure of 1.05–2.8 dB in the frequency range of 13–33 GHz, respectively, while dissipating 99 mW. Full article

Visible-light communication (VLC) has emerged as a promising technology to provide the very high-throughput wireless communications demanded by beyond-fifth-generation (B5G) applications. However, few works are found in the literature regarding the integration of VLC systems with other wireless communications technologies and with access networks. In this context, and as a proof of concept, we implement and experimentally evaluate a hybrid network architecture based on VLC, radio-over-fiber (RoF), free space optics (FSO), fiber-wireless (FiWi), and millimeter-waves (mm-waves) for B5G applications. Such optical networks make use of fiber-optic links based on RoF technology as backhauls, whereas their fronthauls might be either by FSO or RoF. Finally, a triple-wireless-access network is ensured by VLC, FiWi, and mm-wave links. The latter use a real 5G new radio (5G NR) signal. The system performance is evaluated in terms of a root mean square error vector magnitude (EVM_RMS) parameter in accordance with the 3rd-Generation Partnership Project (3GPP) requirements. The experimental results demonstrate a total maximal theoretical throughput of approximately 1.66 Gbps, aligning with the digital performance requirements set by 3GPP. Full article

A gesture recognition method is proposed in this paper, which leverages millimeter-wave radar point clouds, primarily for identifying six basic human gestures. First, the raw radar signals collected by the MIMO millimeter-wave radar are converted into 3D point cloud sequences using a microcontroller integrated into the radar’s baseband processor. Next, based on the SequentialPointNet network, a multiscale feature extraction module is proposed in this paper, which enhances the network’s ability to extract local and global features through convolutional layers at different scales. This compensates for the lack of feature understanding capability caused by single-scale convolution kernels. Moreover, the CBAM in the network is replaced with GAM, which effectively enhances the extraction of global features by more precisely modeling global contextual information, thereby increasing the network’s focus on global features. A separable MLP structure is introduced into the network. The separable MLP operation is used to separately extract local point cloud features and neighborhood features, and then fuse these features, significantly improving the model’s performance. The effectiveness of the proposed method is confirmed through experiments, achieving a 99.5% accuracy in recognizing six fundamental human gestures, effectively distinguishing between gesture categories, and confirming the potential of millimeter-wave radar 3D point clouds in recognizing gestures. Full article

This paper proposes mathematical optimization models for solving the network planning problem using millimeter wave technology for 5G wireless communications networks. To this end, it is assumed that a set of users, $M=\{1,\dots ,m\}$, and a set of base stations, $N=\{1,\dots ,n\}$, are deployed randomly in a square area. In particular, the base stations should be connected, forming a star backbone so that users can connect to their nearest active base stations forming the backbone where the connections are symmetric. In particular, the first two models maximize the number of users connected to the backbone and minimize the distance costs of connecting users to the base stations, and distances of connecting the base stations themselves. Similarly, the last two models maximize and minimize the same objectives and the number of base stations to be activated to form the star backbone. Each user is allowed to connect to a unique active base station. In general, the millimeter wave technology presents a high path loss. Consequently, the transmission distances should be no larger than 300 m at most for different radial transmissions. Thus, a direct line of sight between users and base stations is assumed. Finally, we propose local search-based algorithms that allow finding near-optimal solutions for all our tested instances. Our numerical results indicate that we can solve network instances optimally with up to $k=100$, $n=200$, and $m=5000$ users. Full article

This paper introduces a novel method for measuring the dielectric permittivity of materials within the microwave and millimeter wave frequency ranges. The proposed approach, classified as a guided wave transmission system, employs a periodic transmission line structure characterized by mirror/glide symmetry. The dielectric permittivity is deduced by measuring the transmission properties of such structure when presence of the dielectric material breaks the inherent symmetry of the structure and consequently introduce a stopband in propagation characteristic. To explore the influence of symmetry breaking on propagation properties, an analytical dispersion equation, for both symmetries, is formulated using the Rigorous Coupled Wave Analysis (RCWA) combined with the matrix transverse resonance condition. Based on the analytical equation, an optimization procedure and linearized model for a sensing structure is obtained, specifically for X-band characterization of FR4 substrates. The theoretical results of the model are validated with full wave simulations and experimentally. Full article

In automotive millimeter-wave (MMW) radar systems, achieving high-precision direction of arrival (DOA) estimation with a limited number of array elements is a crucial research focus. Compressive sensing (CS) techniques have been demonstrated to offer superior performance in DOA estimation compared to spectral estimation methods. However, traditional CS methods suffer from an off-grid effect, which causes their reconstruction results to deviate from the actual positions of the signal sources, thereby reducing the accuracy. Currently, as a gridless method, atomic norm minimization (ANM) has shown effectiveness in DOA estimation for uniform linear arrays (ULAs). However, the performance of ANM is suboptimal in non-uniform linear arrays (NULAs), and their computational efficiency is not satisfactory. In this paper, we propose a novel algorithm for DOA estimation in NULA, drawing inspiration from the alternating descent conditional gradient algorithm framework. First, we construct an atomic set based on the observation scene and select the atoms with the highest correlation to the residuals as potential signal sources for global estimation. Then, we construct a mapping function for the signal sources in the continuous domain and perform conditional gradient descent in the neighborhood of each signal source, addressing the bias introduced by the off-grid effect. We compared the proposed algorithm with ANM, Iterative Shrinkage Thresholding (IST), and Multiple Signal Classification (MUSIC) algorithms. Simulation experiments validate that the proposed algorithm effectively addresses the off-grid effect and is applicable to DOA estimation in coprime and random arrays. Furthermore, real data experiments confirm the effectiveness of the proposed algorithm. Full article

Gesture recognition technology based on millimeter-wave radar can recognize and classify user gestures in non-contact scenarios. To address the complexity of data processing with multi-feature inputs in neural networks and the poor recognition performance with single-feature inputs, this paper proposes a gesture recognition algorithm based on ResNet Long Short-Term Memory with an Attention Mechanism (RLA). In the aspect of signal processing in RLA, a range–Doppler map is obtained through the extraction of the range and velocity features in the original mmWave radar signal. Regarding the network architecture in RLA, the relevant features of the residual network with channel and spatial attention modules are combined to prevent some useful information from being neglected. We introduce a residual attention mechanism to enhance the network’s focus on gesture features and avoid the impact of irrelevant features on recognition accuracy. Additionally, we use a long short-term memory network to process temporal features, ensuring high recognition accuracy even with single-feature inputs. A series of experimental results show that the algorithm proposed in this paper has higher recognition performance. Full article

The seamless handover of mobile devices is critical for maximizing the potential of smart city applications, which demand uninterrupted connectivity, ultra-low latency, and performance in diverse environments. Fifth-generation (5G) and beyond-5G networks offer advancements in massive connectivity and ultra-low latency by leveraging advanced technologies like millimeter wave, massive machine-type communication, non-orthogonal multiple access, and beam forming. However, challenges persist in ensuring smooth handovers in dense deployments, especially in higher frequency bands and with increased user mobility. This paper presents an adaptive handover management scheme that utilizes reinforcement learning to optimize handover decisions in dynamic environments. The system selects the best target cell from the available neighbor cell list by predicting key performance indicators, such as reference signal received power and the signal–interference–noise ratio, while considering the fixed time-to-trigger and hysteresis margin values. It dynamically adjusts handover thresholds by incorporating an offset based on real-time network conditions and user mobility patterns. This adaptive approach minimizes handover failures and the ping-pong effect. Compared to the baseline LIM2 model, the proposed system demonstrates a 15% improvement in handover success rate, a 3% improvement in user throughput, and an approximately 6 sec reduction in the latency at 200 km/h speed in high-mobility scenarios. Full article

The efficient acquisition and processing of large-scale terrain data has always been a focal point in the field of photogrammetry. Particularly in complex mountainous regions characterized by clouds, terrain, and airspace environments, the window for data collection is extremely limited. This paper investigates the use of airborne millimeter-wave InSAR systems for efficient terrain mapping under such challenging conditions. The system’s potential for technical application is significant due to its minimal influence from cloud cover and its ability to acquire data in all-weather and all-day conditions. Focusing on the key factors in airborne InSAR data acquisition, this study explores advanced route planning and ground control measurement techniques. Leveraging radar observation geometry and global SRTM DEM data, we simulate layover and shadow effects to formulate an optimal flight path design. Additionally, the study examines methods to reduce synchronous ground control points in mountainous areas, thereby enhancing the rapid acquisition of terrain data. The results demonstrate that this approach not only significantly reduces field work and aviation costs but also ensures the accuracy of the mountain surface data generated by airborne millimeter-wave InSAR, offering substantial practical application value by reducing field work and aviation costs while maintaining data accuracy. Full article