Search Results (497)

Generative artificial intelligence (AI) models including GPT, Gemini, and DeepSeek are reshaping embodied agents, temporal prediction, and autonomous driving, demanding a ten-fold annual growth in training FLOPS that Moore’s law can no longer sustain. Consequently, scale-out GPU clusters require >400 Gb/s lane-rate optical interconnects within AI data-centers (AIDCs). Single-photodiode direct detection offers density, latency, and energy advantages, but DAC bandwidth remains limited to around 70 GHz. We present an optical triple-band multiplexing scheme that replaces high-frequency radio frequency (RF) mixers and local oscillators (LOs) with photonic components. A Mach–Zehnder modulator (MZM) generates 80-GBd PS-PAM-20 signal while an in-phase/quadrature (IQ) modulator driven by a wavelength-offset laser creates two independent 35-GBd PS-64-QAM bands. The proposed optical multiplexing method breaks conjugate symmetry and enhances dispersion tolerance of the direct detection system. After 200 m SSMF transmission and single 70-GHz photodiode (PD) detection, digital signal-signal beating interference (SSBI)/cross-beating compensation enables the recovery of net 543.9 Gb/s signal (line rate of 686.6 Gb/s) using only 45-GHz DACs. The optical multiplexing architecture provides a path to beyond-400 Gb/s lanes and demonstrates a scalable, energy-efficient solution for next-generation AI clusters. Full article

The proliferation of terrestrial and space-based communication systems introduces significant radio frequency interference (RFI), which severely compromises data acquisition for radio telescopes, necessitating robust and dynamic scheduling solutions. This study addresses this challenge by implementing a Deep Recurrent Reinforcement Learning (DRL) framework for the control and dynamic scheduling of the X-Y pedestal-mounted KMITL radio telescope, explicitly trained for RFI avoidance. The methodology involved developing a custom simulation environment with a domain-specific Convolutional Neural Network (CNN) feature extractor and a Long Short-Term Memory (LSTM) network to model temporal dynamics and long-horizon planning. Comparative evaluation demonstrated that the recurrent DRL agent achieved a mean effective survey coverage of 475 deg²/h, representing a 72.7% superiority over the non-recurrent baseline, and maintained exceptional stability with only 1.0% degradation in median coverage during real-world deployment. The DRL framework offers a highly reliable and adaptive solution for telescope scheduling that is capable of maintaining survey efficiency while proactively managing dynamic RFI sources. Full article

Radio Frequency Identification (RFID) is a core enabler of the Industrial Internet of Things (IIoT), yet dense deployments suffer from tag collisions and reader interference that degrade reliability and inflate infrastructure cost. This study proposes a deterministic Reader Deployment Algorithm with Adjustable Reader range (RDA2R) to achieve full tag coverage with minimal interference and reader usage. The method divides the monitored field into grid units, evaluates tag coverage weights, activates high-weight readers with interference checks, and adaptively adjusts interrogation ranges. Simulation results under random and congregation tag distributions show that RDA2R requires about 46–47% fewer readers than ARLDL and 32–33% fewer than MR2D, while improving average tag coverage per reader by over 30%. These results demonstrate that RDA2R provides a scalable, interference-aware, and cost-efficient deployment strategy for RFID-enabled IIoT environments. Full article

Android devices employ several methods to calculate their position. This paper’s focus is the Network Location Provider (NLP), which leverages Wi-Fi and cell tower signals via the fingerprinting/database approach. Unlike GNSS-based positioning, the NLP should be able to compute positions even when the device is indoors or experiencing GNSS radio frequency interference (RFI), making it an enticing candidate for ensuring robust PNT solutions. However, the inner workings of NLP are largely undisclosed, remaining as a ‘black-box’ system. Using the Samsung S24 and Xiaomi Redmi K80 Ultra, we explored the NLP’s response to GNSS spoofing and offline operation (no network connection), as well as attempting NLP spoofing. The GNSS spoofing test confirmed that when satellite signals are spoofed, the NLP solution is maintained at the truth location. This reinforces the robustness of the NLP in RFI environments. In offline mode, NLP continued to operate without a Google server connection, indicating that it can compute positions locally using internally stored cache data. This behavior deviates from the conventional understanding of NLP and offers valuable insights into the latest NLP mechanism. These findings build upon previous work to uncover the inner workings of the NLP and ultimately contribute to robust smartphone PNT. Full article

Phased antenna arrays have revolutionized modern wireless systems by enabling dynamic beamforming, multibeam synthesis, and user tracking to enhance data rates and reduce interferences, yet their reliance on expensive active components (e.g., phase shifters, amplifiers) embedded in antenna array elements limits adoption in cost-sensitive sub-GHz applications. Therefore, the active phased antenna arrays are still considered as high-end technology and primarily designed only for high-frequency bands and demanding applications such as radars and mobile base stations in microwave bands. In contrast, various important radio communication services still operate in sub-GHz bands with no adequate solution for modern antenna systems with beamforming capability. This paper introduces a 3D antenna array with switched-beam or multibeam capability, designed to eliminate mechanical rotators and active circuitry while maintaining all-sky coverage. By integrating collinear radiating elements with a Butler matrix feed network, the proposed 3D array achieves transmit/receive multibeam operation in the 435 MHz amateur satellite band and adjacent 433 MHz ISM band. Simulations demonstrate a design that provides selectable eight beams, enabling horizontal 360° coverage with only one radio connected to the Butler matrix. If eight noncoherent radios are used simultaneously, the proposed antenna array acts as a multibeam all-sky coverage antenna. Innovations in our design include a 3D circular collinear topology combining the broad and adjustable elevation coverage of collinear antennas with azimuthal beam steering, a passive Butler matrix enabling bidirectional transmit/receive multibeam operation, and scalability across sub-GHz bands where collinear antennas dominate (e.g., Lora WAN, trunked radio). Results show sufficient gain, confirming feasibility for low-earth-orbit satellite tracking or long-range IoT backhaul, and maintenance-free beamforming solutions in sub-GHz bands. Given the absence of practical beamforming or multibeam-capable solutions in this frequency band, our novel concept—featuring non-coherent cooperation across multiple ground stations and/or beams—has the potential to fundamentally transform how the growing number of CubeSats in low Earth orbit can be efficiently supported from the ground segment perspective. Full article

Synthetic aperture radar (SAR), as a high-resolution microwave remote sensing imaging technology, plays an indispensable role in both military and civilian applications. However, in complex electromagnetic countermeasure environments, radio frequency interference (RFI) severely degrades SAR imaging quality. SAR anti-interference, as a countermeasure method, has significantly practical values. Although deep learning-based anti-interference techniques have demonstrated notable advantages, two key issues remain unresolved: 1. Strong coupling between interference suppression and SAR imaging—most existing methods rely on raw echo data, leading to a complex processing pipeline and error accumulation. 2. Scarcity of labeled data—the lack of high-quality labeled data severely restricts model deployment. To address these challenges, this work constructs a standardized dataset and conducts comprehensive validation experiments based on this dataset. The main contributions are as follows: Firstly, this work establishes the mathematical model for block-type interference, laying a theoretical foundation for the subsequent construction of RFI-polluted data. Secondly, this work constructs a block-type interference dataset, which includes the labeled data constructed by our laboratory and open-source data from the Sentinel-1 satellites, providing reliable data support for deep learning. Thirdly, this work proposes an image subspace-based interference suppression method, which eliminates the dependence on raw echo data and significantly simplifies the processing pipeline. Finally, this work makes a fair comparison of the current works, summarizes the existing problems, and looks forward to possible future research directions. Full article

Global Navigation Satellite Systems (GNSSs) play a critical role in ensuring the safety of modern transportation across all domains, including aviation, road, rail, and maritime navigation. However, recent years have seen a significant increase in radio frequency interference, including signal masking (jamming) and data deception (spoofing) attacks against GNSSs. These threats can severely compromise human safety, disrupt logistics chains, and undermine essential public services. This study offers a structured holistic overview of the most common forms and impacts of GNSS interference. It also presents practical, resilient solutions to reduce vulnerabilities. Both technological (e.g., redundancy, filtering, alternative navigation) and organizational (e.g., regulation, training, risk assessment) strategies are discussed. The findings highlight that building GNSS resilience is not optional—it is necessary to protect transportation systems that rely on satellite navigation. The summary may be of particular interest to legislators, transport authorities, logistics operators, and policymakers. Full article

Dynamic Spectrum Sharing (DSS) enables spectrum sharing between Long-Term Evolution (LTE) and New Radio (NR) systems, addressing spectrum scarcity in NR. To avoid interference when supporting NR traffic within LTE spectrum, key factors must be compatible. Effective DSS techniques are essential for coexistence. This paper discusses these issues in two parts. Part I covers LTE and NR coexistence using DSS, introducing resource grids, control signals, and channels, and explores DSS approaches for NR data traffic, including NR Synchronization Signal/Physical Broadcast Channels (SSB) transmission via LTE Multicast-Broadcast Single-Frequency Network (MBSFN) and non-MBSFN subframes with associated challenges and standardization efforts for DSS improvement. Part II presents a DSS technique using MBSFN subframes in a heterogeneous network with a macrocell and picocells running on LTE, and in-building small cells running on NR, sharing LTE spectrum via DSS. An optimization problem is formulated to manage traffic through MBSFN allocation, determining the optimal number of MBSFN subframes per LTE frame. System simulations indicate DSS improves Spectral and Energy Efficiency in small cells. The paper concludes with key lessons for LTE and NR coexistence. Full article

Complex distortion cancellation methods are often used at the radio frequency (RF) front end of multiantenna full-duplex transceivers to mitigate signal distortion; however, these methods have high computational complexity and limited practicality. To address these problems, the present study explored the complexities associated with such transceivers to develop a practical multistep approach for suppressing distortions arising from in-phase and quadrature (I/Q) imbalance, nonlinear power amplifier (PA) responses, and multipath self-interference caused by simultaneous transmissions on the same frequency. In this approach, the I/Q imbalance is estimated and then compensated for, following which nonlinear PA distortion is estimated and pre-compensated for. Subsequently, an auxiliary RF transmitter is combined with linearly regenerating self-interference signals to achieve full-duplex self-interference cancellation. The proposed method was implemented on a software-defined radio platform, with the distortion factor calibration specifically optimized for multiantenna full-duplex transceivers. The experimental results indicate that the image signal caused by I/Q imbalance can be suppressed by up to 60 dB through iterative computation. By combining IQI and DPD preprocessing, the nonlinear distortion spectrum can be reduced by 25 dB. Furthermore, integrating IQI, DPD, and self-interference preprocessing achieves up to 180 dB suppression of self-interference signals. Experimental results also demonstrate that the proposed method achieves approximately 20 dB suppression of self-interference. Thus, the method has high potential for enhancing the performance of multiantenna RF full-duplex systems. Full article

Efficient spectrum allocation and telecom traffic steering in densified heterogeneous maritime communication networks remains a critical challenge due to user mobility, dynamic interference, and congestion across terrestrial, aerial, and sea-based transmitters. This paper introduces the Moving Colorable Graph (MCG) framework, a dynamic graph-theoretical representation of interferences that extends conventional graph coloring to capture the spatiotemporal evolution of heterogeneous wireless links under varying channel and traffic conditions. The formulated spectrum allocation problem is inherently non-convex, as it involves discrete frequency assignments, mobility-induced dependencies, and interference coupling among multiple transmitters and users, thus requiring suboptimal yet computationally efficient solvers. The proposed approach models resource assignment as a time-dependent coloring problem, targeting to optimally support users’ diverse demands in dense port-area networks. Considering a realistic port-area network with coastal, sea and Unmanned Aerial Vehicle (UAV) radio coverage, we design and evaluate three MCG-based algorithm variants that dynamically update frequency assignments, highlighting their adaptability to fluctuating demands and heterogeneous coverage domains. Simulation results demonstrate that the selective reuse-enabled MCG scheme significantly decreases network outage and improves user demand satisfaction, compared with static, greedy and heuristic baselines. Overall, the MCG framework may act as a flexible scheme for mobility-aware and congestion-resilient resource management in densified and heterogeneous maritime networks. Full article

In radio frequency (RF) systems, the mixer is a critical component for achieving frequency conversion in electromagnetic sensor backends. This paper proposes a mixer design methodology aimed at improving noise figure and conversion gain specifically for sensor signal processing applications. This design employs a process incorporating high-quality bipolar junction transistors (BJTs) and adopts a mixer-first architecture instead of a conventional low noise amplifier (LNA). By optimizing the layout and symmetry of the BJTs, the input impedance can be flexibly adjusted, thereby simplifying the receiver front-end while simultaneously improving local oscillator (LO) feedthrough. Design and simulation were completed using Advanced Design System (ADS) 2020 software. Simulation results demonstrate that the proposed mixer exhibits significant advantages in suppressing noise and interference while enhancing conversion gain, making it particularly suitable for electromagnetic sensor backend applications. Full article

The rapid growth of the Internet of Things (IoT) has driven the need for ultra-low-power wireless communication systems. Wake-up receivers (WuRXs) have emerged as a key technology to enable energy-efficient, near-always-on operation for IoT devices. This review explores the state of the art in WuRXs design, focusing on low-power architectures, key trade-offs, and recent advancements. We discuss the challenges in achieving low power consumption while maintaining sensitivity, power consumption, and interference resilience. The review highlights the evolution from radio frequency (RF) envelope detection architectures to more complex heterodyne and subthreshold designs and concludes with future directions for WuRXs research. Full article

Sea surface salinity (SSS) observations play a crucial role in the study of ocean circulation, climate variability, and marine ecosystems. However, current satellite SSS products suffer from systematic biases due to factors such as radio frequency interference (RFI) and land contamination, resulting in fundamental limitations to their application for SSS monitoring. To address this issue, we propose a physics-informed neural network (PINN) approach that directly integrates radiative transfer physical processes into the neural network architecture for SMAP L2 SSS bias correction. This method ensures oceanographically consistent corrections by embedding physical constraints into the forward propagation model. The results demonstrate that PINN achieved a root mean square error (RMSE) of 0.249 PSU, representing a 5.3% to 8.5% relative performance improvement compared to conventional methods—GBRT, ANN, and XGBoost. Further temporal stability analysis reveals that PINN exhibits significantly reduced RMSE variations over multi-year periods, demonstrating exceptional long-term correction stability. Meanwhile, this method achieves more uniform bias improvement in contaminated nearshore regions, showing distinct advantages over the inconsistent correction patterns of conventional methods. This study establishes a physics-constrained machine learning framework for satellite SSS data correction by integrating oceanographic domain knowledge, providing a novel technical pathway for reliable enhancement of Earth observation data. Full article

Radio frequency (RF)-based human activity sensing is an essential component of ubiquitous computing, with WiFi sensing providing a practical and low-cost solution for gesture and activity recognition. However, challenges such as manual data collection, multipath interference, and poor cross-domain generalization hinder real-world deployment. Existing data augmentation approaches often neglect the biomechanical structure underlying RF signals. To address these limitations, we present CM-GR, a cross-modal gesture recognition framework that integrates semantic learning with generative modeling. CM-GR leverages 3D skeletal points extracted from vision data as semantic priors to guide the synthesis of realistic WiFi signals, thereby incorporating biomechanical constraints without requiring extensive manual labeling. In addition, dynamic conditional vectors are constructed from inter-subject skeletal differences, enabling user-specific WiFi data generation without the need for dedicated data collection and annotation for each new user. Extensive experiments on the public MM-Fi dataset and our SelfSet dataset demonstrate that CM-GR substantially improves the cross-subject gesture recognition accuracy, achieving gains of up to 10.26% and 9.5%, respectively. These results confirm the effectiveness of CM-GR in synthesizing personalized WiFi data and highlight its potential for robust and scalable gesture recognition in practical settings. Full article

In an attempt to improve utilization of the frequency spectrum left vacant by license holders, cognitive radio networks (CRNs) permit secondary users (SUs) to utilize such spectrum when the license holders, known as primary users (PUs), are inactive. When a pair of SUs wants to communicate over the CRN, they need to converge simultaneously on one of the vacant channels, in a process known as rendezvous. In this work, we attempt to reduce the rendezvous time for SUs executing the well-known enhanced jump-stay (EJS) channel hopping procedure. We achieve this by modifying EJS in order to search the vacant spectrum around a specific favorite channel, instead of hopping across the whole spectrum. Moreover, the search process is carefully designed in order to accommodate the dynamic nature of CRNs, where PUs repeatedly become active and inactive, resulting in disturbances to the rendezvous process. A main feature of our proposed technique, named dynamic jump-stay (DJS), is that the SUs do not need any prior coordination over a common control channel (CCC), thereby allowing for scalable and more robust distributed CRNs. Simulations are used to quantify the resulting performance improvement in terms of expected time to rendezvous, maximum time to rendezvous, and interference on PUs. Full article