Antenna directional pattern measurements can be generally categorized into near-field and far-field measurements. The measurement algorithm designed in this paper is based on the use of Fourier interpolation of band-limited periodic functions under far-field conditions to achieve accurate and fast measurements. The antenna far-field measurement process is generally required to be both accurate and fast, in order to measure accurately, the denser the scanning sampling cutting plane is, the better (the sampling interval for far-field measurement is usually less than 0.1 of the Half Power Beam Width). However, with the development of antenna arrays, there are more and more large aperture phased scanning arrays, and these antennas with narrower half power beam width (HPBW) are sampled very much in 360 degrees scanning range in order to measure accurate pattern in far-field measurement, which obviously reduces the measurement efficiency of the antenna. However, the algorithm proposed in this paper is able to realize the sampling interval as the order of magnitude of HPBW on the basis of Fourier interpolation, which can not only realize the accurate measurement of far-field pattern but also greatly save the measurement time and improve the measurement efficiency. In the paper, several antennas are utilized to prove the feasibility of the algorithm, which shows that the proposed algorithm can not only accurately realize the measurement of the far-field of the directional pattern but also improve the efficiency of the measurement.

This paper presents a broadband slit-coupled antenna design for X-band applications. By using double-layer microstrip patches as the unit, high gain and broadband characteristics have been achieved. In array design, by loading open-circuit branches, the current distribution on the antenna surface was adjusted, thereby reducing the electromagnetic coupling between adjacent units and achieving the expansion of impedance bandwidth. Measurements show a 27.8% impedance bandwidth (9.3-12.3 GHz) with active VSWR < 2. The antenna achieves a maximum gain of 24.98 dBi at 10.8 GHz in the H-plane, with first sidelobe levels below -17 dB, showing good agreement with simulation predictions.

The increasing complexity and dynamism of modern communication networks have driven the need for Network Digital Twins (NDTs)—virtual replicas that enable real-time monitoring, analysis, and predictive management of network behavior. However, realizing operational NDTs requires an integrated data pipeline capable of continuous telemetry ingestion, scalable computation, and intelligent decision support. This paper presents a cloud-native monitoring and prediction framework that enables the creation of functional NDT instances for commercial 5G Core performance management. The system fuses real-time telemetry with machine learning–driven analytics to forecast critical performance indicators such as CPU and memory stress, allowing for proactive fault management and network resource control. Built on a modular and containerized architecture and embedded into Amazon Web Service (AWS), the framework supports seamless deployment and interoperability within Kubernetes environments. Experimental validation on stressing network workloads demonstrates its capability to deliver accurate predictions of system degradation and facilitate closed-loop control strategies. The proposed approach opens a practical path toward the realization of self-optimizing, AI-enhanced network digital twins for next-generation autonomous networks.

Recent advancements in mobile and wireless networks are unlocking the full potential of robotic autonomy, enabling robots to take advantage of ultra-low latency, high data throughput, and ubiquitous connectivity. However, for robots to navigate and operate seamlessly, efficiently and reliably, they must have an accurate understanding of both their surrounding environment and the quality of radio signals. Achieving this in highly dynamic and ever-changing environments remains a challenging and largely unsolved problem. In this paper, we introduce MapViT, a two-stage Vision Transformer (ViT)-based framework inspired by the success of pre-train and fine-tune paradigm for Large Language Models (LLMs). MapViT is designed to predict both environmental changes and expected radio signal quality. We evaluate the framework using a set of representative Machine Learning (ML) models, analyzing their respective strengths and limitations across different scenarios. Experimental results demonstrate that the proposed two-stage pipeline enables real-time prediction, with the ViT-based implementation achieving a strong balance between accuracy and computational efficiency. This makes MapViT a promising solution for energy- and resource-constrained platforms such as mobile robots. Moreover, the geometry foundation model derived from the self-supervised pre-training stage improves data efficiency and transferability, enabling effective downstream predictions even with limited labeled data. Overall, this work lays the foundation for next-generation digital twin ecosystems, and it paves the way for a new class of ML foundation models driving multi-modal intelligence in future 6G-enabled systems.

Infrared small target detection (IRSTD) faces the inherent challenge of precisely localizing dim targets amid complex background clutter. While progress has been made, existing methods usually follow conventional strategies to downsample features and discard small targets’ details, resulting in suboptimal performance. In this paper, we present Na-IRSTD, a native-resolution feature extraction and fusion framework for IRSTD. This framework elegantly incorporates native-resolution features to preserve subtle target cues, overcoming the resolution limitations of existing infrared approaches and significantly improving the model’s ability to localize small targets. We also introduce an effective token reduction and selection strategy, which selects target patches with high accuracy and confidence, boosting the low-level details of the feature while effectively reducing native-resolution patch tokens compared to dense processing, thereby avoiding imposing an unbearable computational burden. Extensive experiments demonstrate the robustness and effectiveness of our token reduction and selection strategy across multiple public datasets. Ultimately, our Na-IRSTD model achieves state-of-the-art performance on four benchmarks.

With the rapid development of 5G technology and the increasing demand for autonomous mobile robots, there is a trend to leverage the ultra-low latency, high data rates, and reliable wireless connectivity offered by 5G to improve the perception and navigation of robots in unknown environments. This paper presents a novel approach for creating and exploiting radio-aware semantic maps to empower 5G-enabled mobile robots operating within an unknown environment. The proposed solution allows for smart offloading of robotic applications and task processing onto the edge systems while facilitating real-time data exchange, and enables robots to gather environment data from both onboard sensors and the mobile network for more efficient robot operation and resource orchestration decisions. A radio-aware semantic mapping framework is introduced, which combines radio signal quality information with semantic mapping techniques to create a comprehensive understanding of the environment, which may evolve over time. The semantic map, enriched with radio quality measurement data, enables mobile robots to make timely informed decisions by considering real-time radio quality variations. Our experimental evaluation demonstrates the effectiveness of adopting radio semantic maps to enhance real-time robot operations on navigation and task offloading in unstructured environments.

In this article, a small-aperture, low-profile, and omnidirectional conformal antenna is proposed which can be utilized on space-limited equipment platforms such as airplanes, ships, and vehicles. The antenna consists of an open metal cavity, a discone antenna, a parasitic structure, and a radome. The small aperture and low-profile design of the metal cavity result in a rapid narrowing of the bandwidth of the discone antenna. Therefore, we introduce a parasitic structure that not only enlarges the impedance bandwidth by adding a resonant point, but can also be used to adjust the unroundness of the horizontal pattern. Meanwhile, the conformal design of the antenna with four surfaces of different curvatures is presented. The simulation and testing results demonstrate that the antenna can achieve a VSWR of less than 2 within a bandwidth of 1.95-2.62 GHz (29.3%), with a minimum aperture of 0.43 omnidirectional radiation pattern, with a gain exceeding -2.2 dBi in the azimuthal plane. This antenna offers the advantages of a small aperture, low profile, and conformal capability. Furthermore, the resonances of high and low frequencies can be adjusted through two different structures, enhancing the flexibility of antenna design.

5G mobile networks introduce a new dimension for connecting and operating mobile robots in outdoor environments, leveraging cloud-native and offloading features of 5G networks to enable fully flexible and collaborative cloud robot operations. However, the limited battery life of robots remains a significant obstacle to their effective adoption in real-world exploration scenarios. This paper explores, via field experiments, the potential energy-saving gains of OROS, a joint orchestration of 5G and Robot Operating System (ROS) that coordinates multiple 5G-connected robots both in terms of navigation and sensing, as well as optimizes their cloud-native service resource utilization while minimizing total resource and energy consumption on the robots based on real-time feedback. We designed, implemented and evaluated our proposed OROS in an experimental testbed composed of commercial off-the-shelf robots and a local 5G infrastructure deployed on a campus. The experimental results demonstrated that OROS significantly outperforms state-of-the-art approaches in terms of energy savings by offloading demanding computational tasks to the 5G edge infrastructure and dynamic energy management of on-board sensors (e.g., switching them off when they are not needed). This strategy achieves approximately similar to 15% energy savings on the robots, thereby extending battery life, which in turn allows for longer operating times and better resource utilization.

The maturity and commercial roll-out of 5G networks and its deployment for private networks makes 5G a key enabler for various vertical industries and applications, including robotics. Providing ultra-low latency, high data rates, and ubiquitous coverage and wireless connectivity, 5G fully unlocks the potential of robot autonomy and boosts emerging robotic applications, particularly in the domain of autonomous mobile robots. Ensuring seamless, efficient, and reliable navigation and operation of robots within a 5G network requires a clear understanding of the expected network quality in the deployment environment. However, obtaining real-time insights into network conditions, particularly in highly dynamic environments, presents a significant and practical challenge. In this paper, we present a novel framework for building a Network Digital Twin (NDT) using real-time data collected by robots. This framework provides a comprehensive solution for monitoring, controlling, and optimizing robotic operations in dynamic network environments. We develop a pipeline integrating robotic data into the NDT, demonstrating its evolution with real-world robotic traces. We evaluate its performances in radio-aware navigation use case, highlighting its potential to enhance energy efficiency and reliability for 5Genabled robotic operations.

Sea surface salinity (SSS) is a fundamental parameter for understanding ocean phenomena and plays a vital role in studying global climate change and weather prediction models. Following the earlier launch of Soil Moisture and Ocean Salinity (SMOS), Aquarius, and Soil Moisture Active Passive (SMAP) satellites, the Chinese Ocean Salinity And Soil Moisture Mission (COSM) was successfully launched on November 14, 2024. The launched satellite is equipped with the 2-D L-band aperture synthesis microwave radiometer (LASMR) and the microwave imager combined active and passive (MICAP) components to gather high-precision SSS information. This article presents the airborne LASMR (ALASMR), which features a Y-shaped 2-D synthetic aperture microwave radiometer and contains 11 antenna units with a unit spacing of $0.82\lambda $ . The ground test investigations of the ALASMR have been conducted to evaluate antenna patterns, test the sensitivity of receiving channels, and conduct ocean aviation experiments. To examine the flight observation, uniform salinity is assumed for the sea area obtained from the calibration platform of National Satellite Ocean Application Service (NSOAS), whereas the salinity gradient is taken for the Laizhou Bay area. The ALASMR exhibits enhanced imaging performance, with a spatial resolution of about 0.35 km and a width of about 1.24 km at the flight altitude of 1.2 km. The retrieval of SSS in the sea area of the ocean calibration platform is also demonstrated in this work. This indicates that ALASMR can reliably execute salinity observation of near-shore with high precision and provide a cross-calibration data source for COSM after its launch.

Cellular-Vehicle-to-Everything (C-V2X) is currently at the forefront of the digital transformation of our society. By enabling vehicles to communicate with each other and with the traffic environment using cellular networks, we redefine transportation, improving road safety and transportation services, increasing efficiency of traffic flows, and reducing environmental impact. This paper proposes a decentralized approach for provisioning Cellular Vehicular-to-Network (C-V2N) services, addressing the coupled problems of service task placement and scaling of edge resources. We formalize the joint problem and prove its complexity. We propose an approach to tackle it, linking the two problems, employing decentralized decision-making using (i) a greedy approach for task placement and (ii) a Deep Deterministic Policy Gradient (DDPG) based approach for scaling. We benchmark the performance of our approach, focusing on the scaling agent, against several State-of-the-Art (SoA) scaling approaches via simulations using a real C-V2N traffic data set. The results show that DDPG-based solutions outperform SoA solutions, keeping the latency experienced by the C-V2N service below the target delay while optimizing the use of computing resources. By conducting a complexity analysis, we prove that DDPG-based solutions achieve runtimes in the range of sub-milliseconds, meeting the strict latency requirements of C-V2N services.

This paper introduces a new concept called MultiX to advance the ongoing discussion on 6G Radio Access Network (RAN) evolution. The pioneering MultiX fusion Perceptive 6G-RAN (MP6R) system integrates multi-sensor, multi-static, multi-band, and multi-technology sensing techniques. Such system is capable of realizing the sought-after multi-sensorial perception, a feature requested by forthcoming 6G applications, and builds on top of three key innovations: i) the MultiX Perception System (MPS) introduces three levels of sensing functions into the RAN stack to support advanced Integrated Sensing and Communication (ISAC) capabilities ; ii) the MP6R Controller (MP6RC) extends RAN control plane functionalities to coordinate and control multi-technology integration, while considering new connectivity approaches and mobility challenges for sensing and localization services; and iii) the Data Access and Security Hub (DASH), a novel data plane entity that aggregates multi-sensor data of diverse technologies, thus providing secure data access, processing, storage, and exposure, ensuring data privacy and trustworthiness.

A novel tightly coupled dual polarized crossed dipole triangular array antenna is designed in this paper. To satisfy certain system requirements, the antenna plane mentioned in this paper has a 2D dimension of 23:13 and is designed only for the frequency range of 8-9 GHz. The printed crossed dipole patch is used as the radiating unit of the antenna, and the matching performance of the antenna unit is effectively improved by a series of measures such as using a balanced balun feed structure for each port and adding parasitic patches above the dipole. To further improve the active VSWR of the antenna unit, resistive branches are loaded at both ends of each dielectric plate. Simulation results show that the 8*8 tightly-coupled triangular phased array composed of this unit (with a spacing of 0) has an active VSWR of less than 2.5 in the frequency range of 8-9 GHz, and is also capable of achieving E-plane, H-plane, and D-plane beam sweeps of ±45° in this frequency range.

A 35-bit electronically-controlled digital substrate integrated waveguide (SIW) phase shifter with high phase resolution and large phase shift range is proposed in this paper. The main part of phase shifter contains two rows of metallic vias in each side as the sidewalls of SIW with different waveguide widths, which can be controlled by loading PIN diodes on the inner row of vias. When the diodes are ON, the inner vias are connected and serve as the sidewalls of SIW. When the diodes are OFF, the inner vias are isolated and the outer vias become the sidewalls. This switching mechanism creates two distinct waveguide widths, resulting in different propagation constants. By altering the length of the SIW with various waveguide widths through the PIN diode states, different phase shifts are generated. The working principle of the phase shifter is thoroughly analyzed, and the prototype is fabricated and measured. The results show that at 4.65GHz, the return loss is larger than 10dB, and the insertion loss varies from 1.2dB to 5dB. Within 36 sets of operating states, the phase shifter achieves a large phase shift range of 262° with a phase shift step of 7.3° which means high phase resolution.

A key distinguishing feature of single flux quantum (SFQ) circuits is that each logic gate is clocked. This feature forces the introduction of path-balancing flip-flops to ensure proper synchronization of inputs at each gate. This paper proposes a polynomial time complexity approximation algorithm for clocking assignments that minimizes the insertion of path balancing buffers for multi-threaded multi-phase clocking of SFQ circuits. Existing SFQ multi-phase clocking solutions have been shown to effectively reduce the number of required buffers inserted while maintaining high throughput, however, the associated clock assignment algorithms have exponential complexity and can have prohibitively long runtimes for large circuits, limiting the scalability of this approach. Our proposed algorithm is based on a linear program (LP) that leads to solutions that are experimentally on average within 5% of the optimum and helps accelerate convergence towards the optimal integer linear program (ILP) based solution. The improved LP and ILP runtimes permit multi-phase clocking schemes to scale to larger SFQ circuits than previous state of the art clocking assignment methods. We further extend the existing algorithm to support fanout sharing of the added buffers, saving, on average, an additional 10% of the inserted DFFs. Compared to traditional full path balancing (FPB) methods across 10 benchmarks, our enhanced LP saves 79.9%, 87.8%, and 91.2% of the inserted buffers for 2, 3, and 4 clock phases respectively. Finally, we extend this approach to the generation of circuits that completely mitigate potential hold-time violations at the cost of either adding on average less than 10% more buffers (for designs with 3 or more clock phases) or, more generally, adding a clock phase and thereby reducing throughput.

The virtualization of Radio Access Networks (vRAN) is well on its way to become a reality, driven by its advantages such as flexibility and cost-effectiveness. However, virtualization comes at a high price — virtual Base Stations (vBSs) sharing the same computing platform incur a significant computing overhead due to in extremis consumption of shared cache memory resources. Consequently, vRAN suffers from increased energy consumption, which fuels the already high operational costs in 5G networks. This paper investigates cache memory allocation mechanisms’ effectiveness in reducing total energy consumption. Using an experimental vRAN platform, we profile the energy consumption and CPU utilization of vBS as a function of the network state (e.g., traffic demand, modulation scheme). Then, we address the high dimensionality of the problem by decomposing it per vBS, which is possible thanks to the Last-Level Cache (LLC) isolation implemented in our system. Based on this, we train a vBS digital twin, which allows us to train offline a classifier, avoiding the performance degradation of the system during training. Our results show that our approach performs very closely to an offline optimal oracle, outperforming standard approaches used in today’s deployments.

This study systematically introduces the development of the world’s first full-link and full-system ground demonstration and verification system for the OMEGA space solar power satellite (SSPS). First, the OMEGA 2.0 innovation design was proposed. Second, field-coupling theoretical models of sunlight concentration, photoelectric conversion, and transmitting antennas were established, and a systematic optimization design method was proposed. Third, a beam waveform optimization methodology considering both a high beam collection efficiency and a circular stepped beam shape was proposed. Fourth, a control strategy was developed to control the condenser pointing toward the sun while maintaining the transmitting antenna toward the rectenna. Fifth, a high-efficiency heat radiator design method based on bionics and topology optimization was proposed. Sixth, a method for improving the rectenna array’s reception, rectification, and direct current (DC) power synthesis efficiencies is presented. Seventh, high-precision measurement technology for high-accuracy beam-pointing control was developed. Eighth, a smart mechanical structure was designed and developed. Finally, the developed SSPS ground demonstration and verification system has the capacity for sun tracking, a high concentration ratio, photoelectric conversion, microwave conversion and emission, microwave reception, and rectification, and thus satisfactory results were obtained.

We report on the theoretical design and experimental verification of a high efficiency microwave wireless power transmission (MWPT) system operating in the Fresnel region. To achieve high conversion efficiency over a transmit distance of 11 meters, the transmit reflector antenna was optimized to locate the focal point at 11 m. The size of the receive antenna was decided by calculating the field distribution and the received power at different positions in the receive antenna aperture. Furthermore, an accurate model of the diode is presented, which was imported into the ADS software for high-precision rectifying circuit design. As a result, an overall DC-DC conversion efficiency of 20% was achieved, as measured in an anechoic chamber at a given distance of 11 m. The experimental results validated the proposed method.

Radio Access Networks virtualization (vRAN) is on its way becoming a reality driven by the new requirements in mobile networks, such as scalability and cost reduction. Unfortunately, there is no free lunch but a high price to be paid in terms of computing overhead introduced by noisy neighbors problem when multiple virtualized base station instances share computing platforms. In this paper, first, we thoroughly dissect the multiple sources of computing overhead in a vRAN, quantifying their different contributions to the overall performance degradation. Second, we design an AI-driven Radio Intelligent Controller (AIRIC) to orchestrate vRAN computing resources. AIRIC relies upon a hybrid neural network architecture combining a relation network (RN) and a deep Q-Network (DQN) such that: (i) the demand of concurrent virtual base stations is satisfied considering the overhead posed by the noisy neighbors problem while the operating costs of the vRAN infrastructure is minimized; and (ii) dynamically changing contexts in terms of network demand, signal-to-noise ratio (SNR) and the number of base station instances are efficiently supported. Our results show that AIRIC performs very closely to an offline optimal oracle, attaining up to 30% resource savings, and substantially outperforms existing benchmarks in service guarantees.

In this paper, a dual-radiation mode leaky-wave antenna (LWA) with flat-topped and end-fire beams based on the composite structure is proposed. A four-element LWA array using substrate integrated waveguide (SIW) technology is designed to generate four -1st space harmonics with different beam pointing angles, which are weighted and superimposed to form a flat-topped and wide-angle beam (including the broadside direction). The structure of printed tapered periodic leaky-wave strips realizes the end-fire directional radiation beam with broadband characteristics. Simulation results demonstrate the effectiveness of the dual-radiation mode composite structure proposed in this paper.