The actual implementation of fifth-generation (5G) and beyond networks faces persistent challenges, including environmental interference and limited coverage, which compromise transmission stability and network feasibility. Reconfigurable Intelligent Surfaces (RISs) have emerged as a promising technology to dynamically reconfigure wireless propagation environments and enhance communication quality. To fully unlock the potential of RIS, this paper proposes a novel deployment strategy based on Double Deep Q-Networks (DDQNs) that jointly optimizes the RIS placement and phase shift configuration to maximize the system sum-rate. Specifically, the coverage area is discretized into a grid, and at each candidate location, a DDQN-based method is developed to solve the corresponding non-convex phase optimization problem. Simulation results reveal that our proposed strategy significantly surpasses conventional benchmark schemes, resulting in a sum-rate improvement of up to 38.41%. The study provides a practical and efficient pre-deployment framework for RIS-enhanced wireless networks.

Introduction High-precision radio wave propagation over maritime environments is of great importance for ensuring reliable maritime wireless communications. Methods To support the development of maritime transmission services, this work employs genetic algorithms to extract features from measured maritime data, thereby constructing a data-model-driven propagation model. The proposed model is established using measurement datasets collected in the South China Sea, covering the frequency range of 99 MHz to 1000 MHz over transmission distances up to 60 km. By integrating the strengths of both data-driven and model-driven approaches, a high-precision empirical model for maritime VHF and UHF propagation loss is developed. Specifically, we first analyze the propagation mechanisms of radio waves in the study region based on the measured data, and then combine them with the ITU-R P.2001 model to define a driving model with undetermined coefficients. These coefficients are subsequently determined using genetic algorithms through feature extraction from the measurement data. Finally, the proposed model is validated against the measurement dataset. Results Results demonstrate that the model achieves an average root-mean-square error of 2.13 dB, representing a 72.73% improvement compared with the ITU-R P.2001 model. Discussion The study of high-precision radio wave propagation over maritime environments is of great importance for ensuring reliable maritime wireless communications.

Reliable wireless connectivity in precision agriculture requires accurate propagation models that remain robust across varying orchard corridor geometries within a given vegetation canopy type. This study characterizes radio propagation at 18GHz (FR3 band) through an extensive measurement campaign (N = 17,269 samples) in a custard apple orchard, systematically varying corridor width [Formula: see text]m and transmitter height [Formula: see text]m across nine geometric configurations. The standard Close-In (CI) model captures the distance-dependent path loss (global fit [Formula: see text]) but leaves substantial geometry-dependent variability unexplained (RMSE = 3.91dB). When the CI model is fitted per configuration, the parameters vary markedly ([Formula: see text] and [Formula: see text]dB), indicating that a single averaged exponent should not be interpreted as geometry-invariant. To address this limitation, we propose a Hybrid Linear+XGBoost framework that combines physically interpretable geometric corrections with nonlinear residual learning. Under leave-one-scenario-out cross-validation, the hybrid model achieves RMSE = 2.97dB, a 24.0% improvement over the CI baseline. Crucially, while pure ensemble methods (Random Forest, Gradient Boosting, XGBoost) exhibit performance degradation up to 1.05dB when extrapolating to unseen geometric configurations, the hybrid architecture demonstrates a 0.35dB improvement, achieving better accuracy on novel corridor configurations than on interpolated data. Corridor width emerged as the dominant factor influencing shadow fading (SF) within the studied custard apple canopy. These findings establish that geometric parameters must be explicitly incorporated into channel models and that hybrid physics-ML architectures offer robust methodological generalization across corridor geometries, whereas the fitted numerical coefficients are expected to depend on vegetation descriptors such as leaf area index, leaf orientation, and moisture content.

Accurate indoor positioning remains a significant challenge due to the unpredictable nature of indoor radio signal propagation. This study presents a novel Wi-Fi fingerprinting-based positioning system using a hybrid deep learning architecture that combines Convolutional Neural Networks (CNN) with Transformer encoders. Unlike traditional algorithms such as KNN, WKNN, SVR, and DeepFi, the proposed CNN-Transformer model leverages the spatial feature extraction capabilities of CNN and the global sequence learning strength of Transformers to enhance indoor positioning accuracy. A unique regression head is integrated to predict precise coordinates directly from raw RSSI input vectors. The proposed CNN-Transformer model outperformed all other algorithms with a Mean position error (MPE) of 1.76 m and a 95th percentile error of 3.2 m. Furthermore, the Cumulative Distribution Function (CDF) analysis revealed that 90% of predictions were within 2.8 m, demonstrating high accuracy and consistency. Although the model incurs higher inference and training times, the significant improvement in accuracy makes it suitable for real-time applications in complex indoor environments. These results underscore the effectiveness of combining CNN and Transformer architectures for robust and scalable indoor localization systems.

Effects of the May 10–13, 2024 extreme magnetic storm in the Asian region of Russia have been studied using experimental data from vertical and oblique sounding of the ionosphere with a continuous chirp signal. Features of ionospheric disturbances induced by the magnetic storm have been revealed: the long-lasting negative ionospheric disturbance that was manifested as a significant decrease in F2-layer critical frequencies and maximum observed frequencies of radio paths; the absence of HF signal reflections from F-region due to sporadic Es layer and increased absorption of HF signals; recording of auroral and oblique Es layers; the long-lasting G-effect during local daytime during which the F1-layer critical frequency exceeded the F2-layer critical frequency; the dusk enhancement of electron density and F2-layer peak height. We have found a correlation of variations in ionospheric parameters and the maximum observed frequencies of HF radio wave propagation modes with spatial location of the main ionospheric trough and the equatorial boundary of the diffuse electron precipitation zone.

Effects of the May 10–13, 2024 extreme magnetic storm in the Asian region of Russia have been studied using experimental data from vertical and oblique sounding of the ionosphere with a continuous chirp signal. Features of ionospheric disturbances induced by the magnetic storm have been revealed: the long-lasting negative ionospheric disturbance that was manifested as a significant decrease in F2-layer critical frequencies and maximum observed frequencies of radio paths; the absence of HF signal reflections from F-region due to sporadic Es layer and increased absorption of HF signals; recording of auroral and oblique Es layers; the long-lasting G-effect during local daytime during which the F1-layer critical frequency exceeded the F2-layer critical frequency; the dusk enhancement of electron density and F2-layer peak height. We have found a correlation of variations in ionospheric parameters and the maximum observed frequencies of HF radio wave propagation modes with spatial location of the main ionospheric trough and the equatorial boundary of the diffuse electron precipitation zone.

Abstract Cold plasma distribution in the ionosphere‐plasmasphere system governs wave‐particle interactions, plasma energization and loss, and radio wave propagation. A longstanding observational gap at altitudes 800–8,000 km has largely prevented studying the coupled dynamics of the two regions. Here, we show that observations by JAXA's Arase mission can bridge this gap. Electron densities inferred from the upper hybrid resonance frequencies measured by Arase are highly consistent with radio occultation profiles from the Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) mission, with a median difference of 5%. Using the combined COSMIC‐Arase data set, we provide a convenient way to reconcile the two regions in empirical models based on the analytical Chapman function inversion for scale height. Our results enable studying fundamental questions about the ionosphere‐plasmasphere coupling, their transition, and life cycle of cold plasma in near‐Earth space.

Reconfigurable Intelligent Surface (RIS)-aided Integrated Sensing and Communication (ISAC) systems are important for next generation wireless networks (e.g., 6G), because it combines RISs capability to intelligently map wireless propagation environments and ISACS capability to realize radar sensing and data transmission at the same frequency band for higher spectrum use and lower hardware cost. The joint beamforming design for these systems is challenging: traditional optimization-based approaches are very time-consuming and cannot be used in real-time applications, while current learning-based methods typically involve large amounts of labeled data, which are expensive and hard to obtain in practice. We present a better unsupervised learning framework, whose primary focus is on designing a new loss function with 3 tunable coefficients {{}}}, , . In particular, coefficient {{}} scales the overall loss gain to keep the optimization running, focuses on the sensing gain term to guide the model to prioritize the echo power for sensing performance, and weights the communication gain term in order to explicitly guarantee user-side link quality. In this way, the proposed design can adaptively balance the sensing signal-to-noise ratio (SNRs) and communication SNRc during training, rather than sacrificing one for the other. Through extensive simulations, we show that compared with the original unsupervised learning scheme, the modified loss function not only achieves a better trade-off between sensing and communication, but also significantly improves SNRs and ensures SNRc meets practical thresholds, while maintaining low computational overhead.

To improve the short-wave antenna based on low-positioned vibrators, an analysis of current trends in the development of new and modernization of existing antenna systems for short-wave radio communications was conducted. The possibility of studying the use of low-positioned horizontal vibrators is due to the fact that it is possible to create rather complex antenna arrays with large gain coefficients from available materials at ionospheric wave antenna sites and not only for them. Approaches to improving antenna systems depending on the type of radio wave propagation, physical parameters of antenna-feeder devices and their directional properties were analyzed. In this regard, an approach to antenna synthesis and the use of this approach in further work to improve the antenna system and obtain the necessary directional characteristics for organizing stable radio communications were substantiated. It is proposed to develop a method of constructive synthesis based on the methods of induced electromotive forces and mirror reflection. At the same time, a set of scientific tasks has been defined to achieve new results, classical mathematical methods have been selected, with the help of which it will be possible to calculate the electromagnetic field created by an improved antenna system. To achieve the set goal, the main directions have been defined, such as: improving the mathematical model of the antenna system based on low-lying radiators; the next step is to improve the mathematical method for calculating the electromagnetic field taking into account the radial components of the electric and magnetic fields; and, as a result, to obtain a convenient tool, to develop a methodology for the constructive synthesis of an improved short-wave antenna system from low-lying radiators with the ability to change the direction of the antenna radiation pattern in the vertical and horizontal planes. The article has formed an objective function that determines the main parameters that need to be achieved. Controlled and uncontrolled variables have been determined, on which the objective function of the specified process directly depends. In conclusion, an approach to calculating the efficiency indicator for evaluating the obtained parameter value from their previous values is presented.

The paper presents experimental studies of anomalous propagation of medium radio waves. Radio echo signals with unusually long delay times of 310–324 ms were recorded at the transmitter point of the sounding signal. The experimental results can be explained by the guiding effect, when a radio wave penetrates into a waveguide channel oriented along the Earth's magnetic field line. In this case, the radio wave propagates to the magnetic conjugated point in the southern hemisphere and returns to the transmission point traveling a distance of 93,000 km. The echo signals were recorded with the use of AARI-developed transmitting and receiving measuring equipment that has a minimum radiation power of 1 kW, while in the previous LDE observation experiments the radiation power was 5 and 17 kW. For the first time, echo signals that radiated from the Earth's surface were recorded not at a single fixed frequency, but in a frequency band of 400 kHz, from 2.100 to 2.400 kHz. The noise environment at frequencies below 2.100 kHz did not allow us to determine the lower boundary of the channel. Analysis of the background geophysical conditions was performed. It was shown that the long delayed echo (LDE) signals were observed under disturbed magnetic conditions (the planetary magnetic index Kp = 4+) in evening hours. The echo signal frequencies exceeded the critical frequencies of the ionosphere at the transmitter point and were less than the critical frequencies at the magnetically conjugate point. A distinctive feature seen from the CADI ionograms was the presence of the F3S layer, which is the main signature of the development of a subauroral polarization stream (SPS) near the station's zenith. Swarm satellite observations revealed that the Gorkovskaya observatory was located at the bottom of the main ionospheric trough (MIT), near its equatorial boundary. The projection of the plasmapause was also located at the MIT bottom, between its polar boundary and Gorkovskaya. Plausible mechanisms for the creation of a waveguide along the magnetic field line were considered. The guiding effect may find practical significance in the development of means and methods for ground-based monitoring of space weather parameters, as well as radar sounding of the near-Earth space.

A variant for improving the accuracy of automated prediction of radio wave propagation is considered given the framework of Recommendation ITU-R P.2001-3. It is noted that in many cases one can improve the accuracy of computations by taking into account the antenna elevation directivity pattern. We pay particular attention to the model of radio wave propagation via sporadic-E reflection. Besides, we analyze the impact of the distance between the terminals as well as the effect that antenna kinds have upon the computation accuracy. We also have found the corrections that would be necessary for typical antennas operating in short- and ultra-short-wave ranges along with the limits, within which these corrections are applicable. We singled out the areas for future research, development of which would further improve the accuracy of predictions concerning the radio wave propagation using the Recommendation ITU-R P.2001.

This paper presents the results of a study of radio wave propagation in underground wine cellars in the context of the optimal use of wireless communication systems for the application of the Internet of Things (IoT) in wine production environments. Electric field strength measurements were carried out in two subterranean line-of-sight (LOS) and non-line-of-sight (NLOS) conditions at 860 MHz, 2400 MHz, and 3600 MHz. The measured results were compared with predictions from seven existing propagation models, including site-general models (Free-space, ITU-R P.1238-13, ITU-R P.1411-12, ETSI TR 138 901 V16.1.0) and site-specific models (ITU-R P.1411-12, tunnel and knife-edge diffraction). Statistical analysis determined that the ITU-R P.1238-13 model, which estimates path loss in corridors, and the tunnel model have the best agreement with measurements in LOS conditions, with average Root Mean Square Error (RMSE) values of 2.8 dB and 3.56 dB, respectively. For the NLOS regions, the knife-edge diffraction model achieved the highest accuracy (average RMSE = 2.77 dB). Furthermore, based on the measurement results, the coefficients of the general path-loss model were adjusted to the data using the least-squares method, yielding RMSEs of 2.02 dB for LOS and 2.65 dB for NLOS conditions. The analysis showed that combining the ITU-R P.1238-13 or tunnel model for LOS conditions with a diffraction-based model for NLOS conditions provides a good basis for modeling radio propagation in subterranean wine cellars. These findings support the design of efficient and robust IoT communication networks in winery environments.

Effective radio resource scheduling at the Medium Access Control (MAC) layer is a critically important task for ensuring quality of service in mobile networks. The use of machine learning and artificial intelligence for MAC-layer scheduling is becoming a promising direction. Existing general-purpose simulators (MATLAB, ns-3, OMNeT++) are insufficiently optimized for in-depth research ofl resource scheduling algorithms and have limitations in their integration. The purpose of this article is to develop a specialized simulation model for LTE (Long Term Evolution) network resource scheduling at the MAC layer for investigating both classical and intelligent scheduling algorithms. The core of the proposed solution lies in creating a modular simulation model that incorporates different user mobility models, radio propagation models, traffic generation models, and classical scheduling algorithms (Round Robin, Proportional Fair, Best CQI). The model specializes in detailed simulation of MAC-layer processes. The system is implemented in Python with modular architecture enabling integration of machine learning and artificial intelligence-based algorithms. The source code is hosted in an open GitHub repository. Experiments were conducted for an infinite buffer simulation scenario with three users from different mobility classes in an urban environment. Three classical scheduling algorithms were tested with evaluation of throughput, Jain's fairness index, and spectral efficiency. The scientific novelty of the solution lies in creating a specialized simulation model optimized for investigating MAC-layer scheduling algorithms with the capability to integrate machine learning methods and providing flexibility in configuring various simulation scenarios. The theoretical significance consists in expanding the toolkit for studying mobile network resource scheduling algorithms and establishing a foundation for developing intelligent schedulers. The practical significance is providing researchers with a specialized tool for developing, testing, and comparing scheduling algorithms, as well as the ability to adapt the model for 5G/6G networks and integrate quality-of-service-aware schedulers.

Abstract Ionospheric density variations can be inferred by studying the effects of electron density structures on transionospheric High Frequency (HF) radio wave propagation. The Radio Receiver Instrument (RRI) on the Enhanced Polar Outflow Probe (e‐POP)/Swarm‐E is used to detect HF radio waves during transionospheric experiments conducted between the space‐based RRI and ground‐based HF transmitters. A Faraday rotation rate‐based method is used to convert RRI HF observations into ionospheric density variations. Two geomagnetically quiet‐day periods in December 2017 are examined, where HF waves from the Ottawa transmitter reveal ionospheric structures with scale sizes from 7 to 750 km. Comparisons with GPS differential Total Electron Content (dTEC) show similar scale sizes. While RRI dTEC agrees with GPS‐derived dTEC for large‐scale features, RRI additionally detects small‐scale fluctuations that are comparable to or surpass the magnitudes of large‐scale variations. Excursions from large‐scale variations observed by RRI are 2 TECU in narrow latitudinal bands of 0.25° corresponding to 25 km. RRI and GPS dTEC variations suggest the continuous existence of 300–350 km scale‐size structures on both days. Moreover the high sampling rate of RRI enhances the measurement capability of small‐scale spatial variations and indicates a quiet‐time ionosphere dominated by small‐scale total electron content variations. RRI measurements give insight into the scale size of localized, transient ionospheric phenomena that affect HF radio wave propagation.

Abstract The plasma around a reentry spacecraft causes the charged particles to interact with the electromagnetic waves emitted by the on-board antennas, and the vehicle experiences radio communication difficulties. A proposed way to mitigate the radio blackout is the magnetic field alleviation technique that consists of superimposing a magnetic field onto the flow, converting the plasma into an anisotropic medium, and changing its refractive index. The applied magnetic field leads to the creation of an extraordinary wave that can propagate for plasma frequencies higher than the radio signal frequency. In this work, a probe containing a cryogenically-cooled high-temperature superconducting magnet is used to study the effect of an applied magnetic field on the plasma flow and on the radio signal propagation, in the VKI Plasmatron facility. The magnetized plasma is characterized by optical emission spectroscopy, stagnation heat flux, and dynamic pressure measurements. The experimental radio signal measurements are conducted using conical horn antennas, operating at frequencies in the K $$_\text{a}$$ a -band. An antenna is placed inside of the magnetic probe, transmitting toward a stagnant air plasma flow. The applied magnetic field causes an increase of the flow temperature, leading to an augmentation of the plasma frequency and stagnation heat flux, due to the Hall effect. No significant effects are observed in the signal transmission and attenuation, while the signal reflection trend is consistent with the variation of magnetic field strength, and plasma and collision frequencies. The dependency of the Faraday rotation with the magnetic field and its direction is observed. While a clear demonstration of the magnetic window is not conclusively observed in the transmission parameters, the behavior of the reflection coefficient shows that the radio blackout mitigation is feasible at optimal combinations of flow ionization.

Effects of weather conditions on radio waves propagation used in mobile communication were studied on some chosen locations in Lagos State. Humidity, temperature, and dust storms were investigated to determine their impact on signal strength, attenuation, and overall network performance. Field measurements and laboratory simulations were used to analyze the effects of weather conditions on radio wave propagation. The results were obtained through field measurements using the network analyzer for signal strength Reference Signal Received Power (RSRS) and speed testing applications for download speed. The temperature, humidity and wind speed for sunny weather condition ranged between for rainy weather, these parameters are in the ranged , in cloudy weather condition, the parameters are in the ranged while the ranged in windy weather conditions are respectively. The results show that 5G networks offer higher download speed (Mbps).

Ham radio has long been a foundational area of practice in electrical engineering. Advances in signal processing, particularly the advent of software-defined radio (SDR), have revolutionized the field, offering new possibilities and modes of operation. This paper introduces a system designed for long-term collection of shortwave propagation data, leveraging SDR technology. It also presents the analysis of the collected data, demonstrating the system’s potential for advancing research in radio wave propagation.

This paper proposes a scheme for enhancing covert communication in cognitive radio networks (CRNs) using a reconfigurable intelligent surface (RIS), which ensures that transmissions by secondary users (SUs) remains statistically undetectable by adversaries (e.g., wardens like Willie). However, there exist stringent challenges in CRNs due to the dual constraints of avoiding detection and preventing harmful interference to primary users (PUs). Leveraging the RIS’s ability to dynamically reconfigure the wireless propagation environment, our scheme jointly optimizes the SU’s transmit power, communication block length, and RIS’s passive beamforming (phase shifts) to maximize the effective covert throughput (ECT) under rigorous covertness constraints quantified by detection error probability or relative entropy while strictly adhering to PU interference limits. Crucially, the RIS configuration is explicitly designed to simultaneously enhance signal quality at the legitimate SU receiver and degrade signal quality at the warden, thereby relaxing the inherent trade-off between covertness and throughput imposed by the fundamental square root law. Furthermore, we analyze the impact of unequal transmit prior probabilities (UTPPs), demonstrating their superiority over equal priors (ETPPs) in flexibly balancing throughput and covertness, and extend the framework to practical scenarios with Poisson packet arrivals typical of IoT networks. Extensive results confirm that RIS assistance significantly boosts ECT compared to non-RIS baselines and establishes the RIS as a key enabler for secure and spectrally efficient next-generation cognitive networks.

Due to the uncontrollability of the channel caused by the uncontrollability of the wireless transmission environment, the introduction of Reconfigurable intelligent surface (RIS) is needed to intelligently control the wireless propagation environment. We use RIS to improve the performance of non-orthogonal multiple access (NOMA)-based Simultaneous wireless information and power transfer (SWIPT) systems. This is done by maximizing the efficiency of energy transfer while ensuring the quality nature of NOMA-based information transmission. To achieve these objectives, we find the optimal phase shift of the RIS using the Semi-Definite Program (SDP) method and determine the optimal NOMA power distribution coefficient using a search approach. The simulation results demonstrate that our scheme improves the energy transfer efficiency compared to the conventional method without RIS.

Evaporation Duct Height (EDH) is a crucial parameter in evaporation duct modeling, as it directly influences the strength of the waveguide trapping effect and significantly impacts the over-the-horizon detection performance of maritime radars. To address the limitations of low prediction accuracy and limited interpretability in existing deep learning models under complex marine meteorological conditions, this study proposes a surrogate model, BLA-EDH, designed to emulate the output of the Naval Postgraduate School (NPS) model for real-time EDH estimation. Experimental results demonstrate that BLA-EDH can effectively replace the traditional NPS model for real-time EDH prediction, achieving higher accuracy than Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) models. Random Forest analysis identifies relative humidity (0.2966), wind speed (0.2786), and 2-m air temperature (0.2409) as the most influential environmental variables, with importance scores exceeding those of other factors. Validation using the parabolic equation shows that BLA-EDH attains excellent fitting performance, with coefficients of determination reaching 0.9999 and 0.9997 in the vertical and horizontal dimensions, respectively. This research provides a robust foundation for modeling radio wave propagation in the Yellow Sea and Bohai Sea regions and offers valuable insights for the development of marine communication and radar detection systems.