Search Results (145)

The rapid advancement of underwater wireless communication technologies is critical to unlocking the full potential of marine resource exploration and environmental monitoring. This paper reviews recent progress in three primary modalities: underwater acoustic communication, radio frequency (RF) communication, and underwater optical wireless communication (UWOC), each designed to address specific challenges posed by complex underwater environments. Acoustic communication, while effective for long-range transmission, is constrained by ambient noise and high latency; recent innovations in noise reduction and data rate enhancement have notably improved its reliability. RF communication offers high-speed, short-range capabilities in shallow waters, but still faces challenges in hardware miniaturization and accurate channel modeling. UWOC has emerged as a promising solution, enabling multi-gigabit data rates over medium distances through advanced modulation techniques and turbulence mitigation. Additionally, bio-inspired approaches such as electric field communication provide energy-efficient and robust alternatives under turbid conditions. This paper further examines the practical integration of these technologies in underwater platforms, including autonomous underwater vehicles (AUVs), highlighting trade-offs between energy efficiency, system complexity, and communication performance. By synthesizing recent advancements, this review outlines the advantages and limitations of current underwater communication methods and their real-world applications, offering insights to guide the future development of underwater communication systems for robotic and vehicular platforms. Full article

In visible light communication (VLC) systems, lenses are typically used to collimate light at the transmitter. However, due to the wide light emission angle of mini-LEDs, capturing light at large angles using a lens at the transmitter can be challenging. This paper presents a design of a reflective cup at the mini-LED-based VLC transmitter. The redesigned reflective cup can collect most of the light and collimate it, achieving an efficiency of approximately 86% at a distance of 10 m in the simulation. In the experiment, error-free communication was achieved at a distance of 100 m with a data rate of 190 Mbps. To the best of our knowledge, a long-distance VLC system based on mini-LEDs is investigated for the first time. The reflective cup offers advantages, including high efficiency, low cost, and a simple structure. It holds reference value for addressing the issue of limited communication distance in underwater wireless optical communication (UWOC). Full article

High demand for 6G wireless has made photonics-aided D-band (110–170 GHz) communication a research priority. Photonics-aided technology integrates optical and wireless communications to boost spectral efficiency and transmission distance. This study presents a Radio-over-Fiber (RoF) communication system utilizing photonics-aided technology for 4600 m long-distance D-band transmission. We successfully show the transmission of a 50 Gbps (25 Gbaud) QPSK signal utilizing a 128.75 GHz carrier frequency. Notwithstanding these encouraging outcomes, RoF systems encounter considerable obstacles, including pronounced nonlinear distortions and phase noise related to laser linewidth. Numerous factors can induce nonlinear impairments, including high-power amplifiers (PAs) in wireless channels, the operational mechanisms of optoelectronic devices (such as electrical amplifiers, modulators, and photodiodes), and elevated optical power levels during fiber transmission. Phase noise (PN) is generated by laser linewidth. Despite the notable advantages of classical Volterra series and deep neural network (DNN) methods in alleviating nonlinear distortion, they display considerable performance limitations in adjusting for phase noise. To address these problems, we propose a novel post-processing approach utilizing a two-dimensional convolutional neural network (2D-CNN). This methodology allows for the extraction of intricate features from data preprocessed using traditional Digital Signal Processing (DSP) techniques, enabling concurrent compensation for phase noise and nonlinear distortions. The 4600 m long-distance D-band transmission experiment demonstrated that the proposed 2D-CNN post-processing method achieved a Bit Error Rate (BER) of 5.3 × 10⁻³ at 8 dBm optical power, satisfying the soft-decision forward error correction (SD-FEC) criterion of 1.56 × 10⁻² with a 15% overhead. The 2D-CNN outperformed Volterra series and deep neural network approaches in long-haul D-band RoF systems by compensating for phase noise and nonlinear distortions via spatiotemporal feature integration, hierarchical feature extraction, and nonlinear modelling. Full article

LoRa, as a leading LPWAN technology, plays a pivotal role in enabling long-range, low-power wireless communication, especially in agricultural IoT applications. This study examines the propagation characteristics of 433 MHz LoRa signals in maize fields, focusing on signal attenuation, RSSI, SNR, and packet loss under dense crop conditions. Field experiments were conducted in Wuwei, Gansu Province, with validation tests in Tongliao, Inner Mongolia. The effects of transmitter and receiver antenna heights on signal quality and propagation distance were systematically analyzed. Results show a consistent improvement in signal quality and range with increased antenna height. Path loss models were developed using regression analysis, achieving high predictive accuracy (R² > 0.9). Validation confirmed the models’ reliability, offering valuable insights for deploying wireless sensor networks (WSNs) in agriculture. Future research will integrate machine learning for dynamic modeling and explore variations across crop growth stages. Full article

This article presents the decision feedback equalizer (DFE), the maximum likelihood detection (MLD), and the radius-directed equalization (RDE) algorithms designed in MATLAB-R2018a to equalize the received signal in a dispersive optical link up to 120 km. DFE is essential for improving signal quality in several communication systems, including WiFi networks, cable modems, and long-term evolution (LTE) systems. Its capacity to mitigate inter-symbol interference (ISI) and rapidly adjust to channel variations renders it a flexible option for high-speed data transfer and wireless communications. Conversely, MLD is utilized in applications that require great precision and dependability, including multi-input–multi-output (MIMO) systems, satellite communications, and radar technology. The ability of MLD to optimize the probability of accurate symbol detection in complex, high-dimensional environments renders it crucial for systems where signal integrity and precision are critical. Lastly, RDE is implemented as an alternative algorithm to the CMA-based equalizer, utilizing the idea of adjusting the amplitude of the received distorted symbol so that its modulus is closer to the ideal value for that symbol. The algorithms are tested using a converged 5G mm-wave analog radio-over-fiber (A-RoF) system at 60 GHz. Their performance is measured regarding error vector magnitude (EVM) values before and after equalization for different optical fiber lengths and modulation formats (QPSK, 16-QAM, 64-QAM, and 128-QAM) and shows a clear performance improvement of the output signal. Moreover, the performance of the proposed algorithms is compared to three commonly used algorithms: the simple least mean square (LMS) algorithm, the constant modulus algorithm (CMA), and the adaptive median filtering (AMF), demonstrating superior results in both QPSK and 16-QAM and extending the transmission distance up to 120 km. DFE has a significant advantage over LMS and AMF in reducing the inter-symbol interference (ISI) in a dispersive channel by using previous decision feedback, resulting in quicker convergence and more precise equalization. MLD, on the other hand, is highly effective in improving detection accuracy by taking into account the probability of various symbol sequences achieving lower error rates and enhancing performance in advanced modulation schemes. RDE performs best for QPSK and 16-QAM constellations among all the other algorithms. Furthermore, DFE and MLD are particularly suitable for higher-order modulation formats like 64-QAM and 128-QAM, where accurate equalization and error detection are of utmost importance. The enhanced functionalities of DFE, RDE, and MLD in managing greater modulation orders and expanding transmission range highlight their efficacy in improving the performance and dependability of our system. Full article

Channel modeling of seawater is essential for understanding the transmission process of underwater laser light and optimizing the system design of underwater wireless laser communication. This study systematically examined the transmission characteristics of underwater blue-green laser communication, such as the angle of arrival, beam spreading, and channel loss, based on the Monte Carlo ray tracing method, across three different waters. The statistical analysis has led to the following definitive conclusions: (a) The differences in average AOA are profound in clear water and at short attenuation lengths in coastal and turbid harbor waters and are small at long attenuation lengths. The differences in average AOA between the offsets of 0 m and 10 m are about 62.3° and 12.9° at the attenuation lengths of 1 and 25 in clear water. The differences between offsets of 0 m and 10 m in average AOAs are about 74.4° and 5.8° in coastal water and 67.2° and 12.2° in turbid harbor water at the attenuation lengths of 1, 20, and 35, respectively. (b) The beam diameters are 0.1 m at the attenuation length of 25 in clear water and 83.8 m and 25.3 m when the attenuation length is 35 in coastal and turbid harbor waters. It manifests that the beam spreading is indistinctive in clear water while prominent in coastal and turbid harbor waters. (c) The difference in the received power at the various offsets decreases with increasing attenuation length but with distinct patterns. Take the offsets of 0 m and 10 m as examples. The absolute difference in the power loss reduces from 88.0 dB·m⁻² to 46.8 dB·m⁻² when the attenuation length reaches 25 in clear water. At the attenuation lengths of 1 and 35, the power losses are 94.9 dB·m⁻² and 4.3 dB·m⁻² in coastal water and 117.4 dB·m⁻² and 12.6 dB·m⁻² in turbid harbor water. Moreover, the minimum underestimation of power loss by applying Beer’s Law could be almost 2 dB·m⁻² in turbid harbor waters. To achieve a high receiving gain, the weighted average angles of arrival at different offsets indicate that a small field of view is advantageous in clear water and at short transmission distances in coastal and turbid harbor waters. In contrast, a larger field of view is effective at long transmission distances in coastal and turbid harbor waters. Additionally, the absolute differences in channel losses at various offsets suggest that alignment between the transmitter and the receiver is crucial in clear water and at short transmission distances in coastal and turbid harbor waters. In contrast, misalignment may not lead to significant channel loss at longer transmission distances in turbid harbor water. The results of this study underscore the importance of considering water type, transmission distance, and offsets relative to the beam center when selecting receiver parameters. Full article

Many important practical problems connected to energy efficiency in buildings, ecology, metallurgy, the development of wireless communication systems, the optimization of radar technology, quantum computing, pharmacology, and seismology are described by large-scale mathematical models that are typically represented by systems of partial differential equations. Such systems often involve numerous input parameters. It is crucial to understand how susceptible the solutions are to uncontrolled variations or uncertainties within these input parameters. This knowledge helps in identifying critical factors that significantly influence the model’s outcomes and can guide efforts to improve the accuracy and reliability of predictions. Sensitivity analysis (SA) is a method used efficiently to assess the sensitivity of the output results from large-scale mathematical models to uncertainties in their input data. By performing SA, we can better manage risks associated with uncertain inputs and make more informed decisions based on the model’s outputs. In recent years, researchers have developed advanced algorithms based on the analysis of variance (ANOVA) technique for computing numerical sensitivity indicators. These methods have also incorporated computationally efficient Monte Carlo integration techniques. This paper presents a comprehensive theoretical and experimental investigation of Monte Carlo algorithms based on “symmetrized shaking” of Sobol’s quasi-random sequences. The theoretical proof demonstrates that these algorithms exhibit an optimal rate of convergence for functions with continuous and bounded first derivatives and for functions with continuous and bounded second derivatives, respectively, both in terms of probability and mean square error. For the purposes of numerical study, these approaches were successfully applied to a particular problem. A specialized software tool for the global sensitivity analysis of an air pollution mathematical model was developed. Sensitivity analyses were conducted regarding some important air pollutant levels, calculated using a large-scale mathematical model describing the long-distance transport of air pollutants—the Unified Danish Eulerian Model (UNI-DEM). The sensitivity of the model was explored focusing on two distinct categories of key input parameters: chemical reaction rates and input emissions. To validate the theoretical findings and study the applicability of the algorithms across diverse problem classes, extensive numerical experiments were conducted to calculate the main sensitivity indicators—Sobol’ global sensitivity indices. Various numerical integration algorithms were employed to meet this goal—Monte Carlo, quasi-Monte Carlo (QMC), scrambled quasi-Monte Carlo methods based on Sobol’s sequences, and a sensitivity analysis approach implemented in the SIMLAB software for sensitivity analysis. During the study, an essential task arose that is small in value sensitivity measures. It required numerical integration approaches with higher accuracy to ensure reliable predictions based on a specific mathematical model, defining a vital role for small sensitivity measures. Both the analysis and numerical results highlight the advantages of one of the proposed approaches in terms of accuracy and efficiency, particularly for relatively small sensitivity indices. Full article

Conceptually, this paper aims to help reduce the communication blind spots originating from the design of millimeter-wave (mmW) beamforming by deploying radio units of an open radio access network (O-RAN) with free-space optics (FSOs) as the backhaul and the fiber-optic link as the fronthaul. At frequencies exceeding 24 GHz, the transmission reach of 5G/6G beamforming is limited to a few hundred meters, and the periphery area of the sector operational range of beamforming introduces a communication blind spot. Using FSOs as the backhaul and a fiber-optic link as the fronthaul, O-RAN empowers the radio unit to extend over greater distances to supplement the communication range that mmW beamforming cannot adequately cover. Notably, O-RAN is a prime example of next-generation wireless networks renowned for their adaptability and open architecture to enhance the cost-effectiveness of this integration. A 200 meter-long FSO link for backhaul and a fiber-optic link of up to 10 km for fronthaul were erected, thereby enabling the reach of communication services from urban centers to suburban and remote rural areas. Furthermore, in the context of beamforming, reinforcement learning (RL) was employed to optimize the error vector magnitude (EVM) by dynamically adjusting the beamforming phase based on the communication user’s location. In summary, the integration of RL-based mmW beamforming with the proposed O-RAN communication setup is operational. It lends scalability and cost-effectiveness to current and future communication infrastructures in urban, peri-urban, and rural areas. Full article

Wireless passive temperature sensors are essential in environments where wired connections are impractical, such as rotating machinery and harsh conditions. A key advantage of these sensors is their ability to operate without a local power source. This study employs the antenna backscattering method, which relies on the wireless interaction between the interrogator antenna and the sensor antenna’s resonant frequency, implemented in the far-field region to support long communication distances. To evaluate the impact of antenna shape on sensor performance, three microstrip patch antenna shapes—rectangular, circular, and equilateral triangular—were designed to operate in the fundamental mode at 2.4 GHz. These designs were simulated using HFSS in Ansys Electromagnetic Suite^® 2023 R1 (Ansys Inc., Canonsburg, PA, USA), fabricated on alumina substrates, and assessed for performance metrics, including communication distance and sensitivity. Results indicated that the equilateral triangular patch outperformed the others, achieving a maximum communication distance of 16.5 cm, a sensitivity of 0.129 MHz/°C over a temperature range of 25 °C to 500 °C, and a simulated gain of 5.84 dBi. These findings underscore the importance of antenna shape selection and optimization for robust, wireless temperature sensing in demanding environments. Full article

We describe a quantum teleportation protocol for exchanging data between a mobile robot and its control station. Because of the high cost of quantum network systems, we use MATLAB software to simulate the teleportation of data. Our simulation models the dynamic motion of a car-like mobile robot (CLMR), considering its mass and inertia and the environmental viscosity. Our remote control method accurately reproduces a mathematical model of the CLMR’s real-world motion. The CLMR’s trajectory is represented by differential equations, with the velocity calculated using the Jacobian matrix. The velocity inputs are teleported from the control station to the CLMR, enabling it to move. Nevertheless, physical constraints cause the deviation of the robot’s trajectory from the predicted trajectory. To correct this deviation, the CLMR’s current position is teleported to the control station. Before implementing this protocol, we calculate the quantum teleportation circuit, and we use quantum gates in matrix form to simulate the data teleportation process. The protocol’s accuracy is assessed by comparing the original data and teleported data, and a good match is obtained. This study demonstrates the feasibility of quantum teleportation for remotely controlling real-time robotic systems over long distances and in environments that interfere with classical wireless communication. Full article

In response to the current key issues in the field of smart irrigation for farmland, such as the lack of data sources and insufficient integration, a low degree of automation in drive execution and control, and over-reliance on cloud platforms for analyzing and calculating decision making processes, we have developed nodes and gateways for smart irrigation. These developments are based on the EC-IOT edge computing IoT architecture and long range radio (LoRa) communication technology, utilizing STM32 MCU, WH-101-L low-power LoRa modules, 4G modules, high-precision GPS, and other devices. An edge computing analysis and decision model for smart irrigation in farmland has been established by collecting the soil moisture and real-time meteorological information in farmland in a distributed manner, as well as integrating crop growth period and soil properties of field plots. Additionally, a mobile mini-program has been developed using WeChat Developer Tools that interacts with the cloud via the message queuing telemetry transport (MQTT) protocol to realize data visualization on the mobile and web sides and remote precise irrigation control of solenoid valves. The results of the system wireless communication tests indicate that the LoRa-based sensor network has stable data transmission with a maximum communication distance of up to 4 km. At lower communication rates, the signal-to-noise ratio (SNR) and received signal strength indication (RSSI) values measured at long distances are relatively higher, indicating better communication signal quality, but they take longer to transmit. It takes 6 s to transmit 100 bytes at the lowest rate of 0.268 kbps to a distance of 4 km, whereas, at 10.937 kbps, it only takes 0.9 s. The results of field irrigation trials during the wheat grain filling stage have demonstrated that the irrigation amount determined based on the irrigation algorithm can maintain the soil moisture content after irrigation within the suitable range for wheat growth and above 90% of the upper limit of the suitable range, thereby achieving a satisfactory irrigation effect. Notably, the water content in the 40 cm soil layer has the strongest correlation with changes in crop evapotranspiration, and the highest temperature is the most critical factor influencing the water requirements of wheat during the grain-filling period in the test area. Full article

Underwater optical communication has emerged as an essential tool for exploring oceanography and marine resources for underwater vehicles or robots in recent years. Current techniques mostly rely on the paradigm of intensity modulation and direct detection, resorting to more powerful light sources on the transmitting side and more sensitive detectors on the receiving side, thus causing excess energy consumption and system costs. Here, a novel approach, namely, the optical differential communications method (ODCM), is proposed to extend the distance of underwater wireless optical communications in turbid water. The underlying physical reason is explained in theory and demonstrated in experiments. It is found that the stable propagation distance of ODCM could be further extended without relying on intensive light sources, in contrast to conventional methods, showing potential for longer communication ranges. Tests of underwater optical communications are conducted, and the results show that ODCM can significantly reduce the bit error rate (BER) at the same propagation distance or extend the propagation distance for the same BER level of optical signals. As such, this study provides an avenue for long-distance and stable underwater wireless optical communications in turbid environments. Full article

Marine life exploration is constrained by factors such as limited scuba diving time, depth restrictions for divers, costly expeditions, safety risks to divers’ health, and minimizing harm to marine ecosystems, where traditional diving often risks disturbing marine life. This paper introduces Nu (named after an ancient Egyptian deity), a 3D-printed Remotely Operated Underwater Vehicle (ROUV) designed in an attempt to address these challenges. Nu employs Long Range (LoRa), a low-power and long-range communication technology, enabling wireless operation via a manual controller. The vehicle features an onboard live-feed camera with a separate communication system that transmits video to an external real-time machine learning (ML) pipeline for fish species classification, reducing human error by taxonomists. It uses Brushless Direct Current (BLDC) motors for long-distance movement and water pump motors for precise navigation, minimizing disturbance, and reducing damage to surrounding species. Nu’s functionality was evaluated in a controlled 2.5-m-deep body of water, focusing on connectivity, maneuverability, and fish identification accuracy. The fish detection algorithm achieved an average precision of 60% in identifying fish presence, while the classification model achieved 97% precision in assigning species labels, with unknown species flagged correctly. The testing of Nu in a controlled environment has met the system design expectations. Full article

Unmanned aerial vehicle (UAV)-based wireless sensor networks (WSNs) hold great promise for supporting ground-based sensors due to the mobility of UAVs and the ease of establishing line-of-sight links. UAV-based WSNs equipped with mobile edge computing (MEC) servers effectively mitigate challenges associated with long-distance transmission and the limited coverage of edge base stations (BSs), emerging as a powerful paradigm for both communication and computing services. Furthermore, incorporating simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) as passive relays significantly enhances the propagation environment and service quality of UAV-based WSNs. However, most existing studies place STAR-RISs in fixed positions, ignoring the flexibility of STAR-RISs. Some other studies equip UAVs with STAR-RISs, and UAVs act as flight carriers, ignoring the computing and caching capabilities of UAVs. To address these limitations, we propose an energy-efficient aerial STAR-RIS-aided computing offloading and content caching framework, where we formulate an energy consumption minimization problem to jointly optimize content caching decisions, computing offloading decisions, UAV hovering positions, and STAR-RIS passive beamforming. Given the non-convex nature of this problem, we decompose it into a content caching decision subproblem, a computing offloading decision subproblem, a hovering position subproblem, and a STAR-RIS resource allocation subproblem. We propose a deep reinforcement learning (DRL)–successive convex approximation (SCA) combined algorithm to iteratively achieve near-optimal solutions with low complexity. The numerical results demonstrate that the proposed framework effectively utilizes resources in UAV-based WSNs and significantly reduces overall system energy consumption. Full article

To address the issues of pipeline corrosion and erosion during ground testing, this paper presents an innovative electromagnetic ultrasonic thickness measurement system that utilizes ZigBee wireless communication technology. The system employs a ZigBee mesh topology for creating a wireless distributed network, where node devices carry out multi-point monitoring in a configuration of “one master, multiple”. Each node is powered by an STM32 embedded control chip and fitted with ultrasonic sensors. Slave nodes transmit the real-time data they collect to a server via the master node, thus enabling remote monitoring of the system through a web interface. The system incorporates an enhanced data filtering algorithm, allowing for precise monitoring of the pipeline wall thickness and providing immediate data feedback. An experimental validation of the system’s stability and long-distance transmission capabilities was performed on a simulated platform, confirming its viability and applicability for real-world engineering applications. Full article