Transmitter Locations Research Articles

ACT‐GAN: Radio map construction based on generative adversarial networks with ACT blocks

AbstractThe radio map serves as a vital tool in assessing wireless communication networks and monitoring radio coverage, providing a visual representation of electromagnetic spatial characteristics. To address the limitation of low accuracy in current radio map construction method, this article presents a novel method based on Generative Adversarial Network (GAN), called ACT‐GAN. This method incorporates the aggregated contextual‐transformation block, the convolutional block attention module, and the transposed convolutional block into the generator, significantly enhancing the construction accuracy of radio map. The performance of ACT‐GAN is validated in three distinct scenarios. The results indicate that, in scenario 1, where the transmitter locations are known, the average reduction in Root Mean Square Error (RMSE) is 14.6%. In scenario 2, where the transmitter locations are known and supplemented with sparse measurement maps, the average reduction in RMSE is 13.3%. Finally, in scenario 3, where the transmitter locations are unknown, the average reduction in RMSE is 7.1%. Moreover, the proposed model exhibits clearer predictive results and can accurately capture multi‐scale shadow fading.

Performance Evaluation of Radio Frequency Visualisation Tool for Wireless Communications: A UNIUYO Campus Case Study

This study was motivated by the challenge of evaluating radio frequency (RF) signal behaviour concerning transmitter and receiver positioning prior to deployment and during troubleshooting. This paper presented the development and testing of a MATLAB-based signal visualisation application to enhance RF signal analysis across various frequencies and terrains, specifically focusing on the University of Uyo (UNIUYO) permanent site and selected locations in Uyo. The application featured three distinct tabs with specialised functions. The first tab integrated campus radio broadcasts at 144.5 MHz and visualised signals for mobile communication standards (2G, 3G, and 4G). The second tab offered a computation interface for selected radio propagation parameters and a tool for converting geographic coordinates from degrees, minutes, and seconds (DMS) to decimal degrees (DD). The third tab was dedicated to 5G testing and deployment. To determine the signal travel extent for each transmitter location, the Longley-Rice and Freespace propagation models were used to represent worst-case and best-case scenarios, respectively. The results, illustrated graphically, demonstrated the application’s effectiveness and versatility in RF signal analysis and 5G deployment planning. The study explored two primary 5G deployment strategies: the standalone (SA) approach and the non-standalone (NSA) technique. The SA strategy employed mid-band frequency (3.5 GHz) point-to-multipoint stations within clusters. The application utilised up to 19 transmitters (one main transmitter and 18 repeaters) per cluster. Conversely, the NSA technique integrated 5G technology with the existing 4G infrastructure to enhance coverage. The findings offer valuable insights into optimising 5G deployment in terrains similar to those at the UNIUYO permanent site and Uyo in general.

Fast Selection of Indoor Wireless Transmitter Locations With Generalizable Neural Network Propagation Models

Fast Selection of Indoor Wireless Transmitter Locations With Generalizable Neural Network Propagation Models

Deep Learning-Based Transmitter Localization in Sparse Wireless Sensor Networks.

In the field of wireless communication, transmitter localization technology is crucial for achieving accurate source tracking. However, the extant methodologies for localization face numerous challenges in wireless sensor networks (WSNs), particularly due to the constraints posed by the sparse distribution of sensors across large areas. We present DSLoc, a deep learning-based approach for transmitter localization in sparse WSNs. Our method is based on an improved high-resolution network model in neural networks. To address localization in sparse wireless sensor networks, we design efficient feature enhancement modules, and propose to locate transmitter locations in the heatmap using an image centroid-based method. Experiments conducted on WSNs with a 0.01% deployment density demonstrate that, compared to existing deep learning models, our method significantly reduces the transmitter miss rate and improves the localization accuracy by more than double. The results indicate that the proposed method offers more accurate and robust performance in sparse WSN environments.

Indoor unknown radio transmitter localization using improved RSSD and grey correlation degree

Abstract Accurate location of unknown radio transmitter (URT) is the key to secure wireless communication. Since the fingerprint positioning methods based on received signal strength difference (RSSD) can adapt to the diversity of transmitting power and frequency, RSSD has become a popular scheme for locating the unknown radio transmitter. However, the RSSD is obtained by subtracting the RSS from two different access points (APs), so the interference of noise on the RSS is inherited and amplified by the RSSD. Besides, the need for more APs to ensure positioning accuracy leads to an increase in hardware costs. In this paper, a RSSD-based fuzzy weight grey correlation degree positioning algorithm, called FUZZY-GREY, is proposed to reduce the interference of noise, save AP hardware cost and improve the positioning accuracy. Firstly, online RSSD vector is improved by using fuzzy weight to reduce the noise interference. Secondly, the RSSD-based grey correlation coefficient is designed to calculate the correlation degree of the corresponding RSSD and ensure data integrity. Finally, a RSSD-based grey correlation degree scheme combining with fuzzy weight is proposed to select optimal reference points (RPs). Simulation and experimental results show that the proposed algorithm has better positioning performance than weighted k-nearest neighbor (WKNN), maximum correlation coefficient estimation (MCORE), Naive Bayes and support vector machine (SVM) in the case of different selected K numbers, grid distances, noise levels and AP numbers.

Understanding Wireless ChannelsThrough NeRF2

Despite Maxwell's formulation of the electromagnetic wave laws over a century and a half ago, accurately modeling the transmission of RF signals within electrically complex environments continues to be a formidable challenge. This complexity arises from intricate interactions of RF signals with various obstacles, such as reflections and diffractions. Inspired by the effective utilization of neural networks in computer vision for representing optical fields, this paper introduces NeRF2, a novel neural radio-frequency radiance field. NeRF2 conceptualizes a continuous, volumetric scene function to interpret RF signal propagation. Uniquely, NeRF2, once trained with limited signal measurements, can predict the characteristics and reception of a signal at an arbitrary point, given the transmitter's location. Functioning as a physical-layer neural network, NeRF2 amalgamates learned statistical models with physical ray tracing to create synthetic datasets, tailored for the training needs of application-layer artificial neural networks (ANNs). This integration, termed "turbo-learning," combines authentic and synthetic datasets, significantly augmenting ANNs' training efficacy. Experimental results illustrate that turbo-learning can substantially improve performance, with an estimated 50% enhancement. The efficacy of NeRF2 is further underscored in its applications in indoor localization and 5G MIMO systems.

A Deep-Learning Approach to a Volumetric Radio Environment Map Construction for UAV-Assisted Networks

Providing global coverage for ubiquitous users is a key requirement of the fifth generation (5G) and beyond wireless technologies. This can be achieved by integrating airborne networks, such as unmanned aerial vehicles (UAVs) and satellite networks, with terrestrial networks. However, the deployment of airborne networks in a three-dimensional (3D) or volumetric space requires a new understanding of the propagation channel and its losses in both the areal and altitude dimensions. Despite significant research on radio environment map (REM) construction, much of it has been limited to two-dimensional (2D) contexts. This neglects the altitude-related characteristics of electromagnetic wave propagation and confines REMs to 2D formats, which limits the comprehensive and continuous visualization of propagation environment variation in spatial dimensions. This paper proposes a volumetric REM (VREM) construction approach to compute 3D propagation losses. The proposed approach addresses the limitations of existing approaches by learning the spatial correlation of wireless propagation channel characteristics and visualizing REM in areal and height/altitude dimensions using deep learning models. Specifically, the approach uses two deep learning-based models: volume-to-volume (Vol2Vol) VREM with 3D-generative adversarial networks and sliced VREM with altitude-aware spider-UNets. In both cases, knowledge of the propagation environment and transmitter locations in 3D space is used to capture the spatial and altitude dependency of the propagation channel’s characteristics. We developed the Addis dataset, a large REM dataset comprising 54,000 samples collected from the urban part of Addis Ababa, Ethiopia, to train the proposed models. Each sample of data comprises a 512-meter by 512-meter areal resolution with different 3D obstacles (buildings and terrain), 15 simulated propagation loss maps at every 3-meter altitude resolution, and 80 different 3D transmitter locations. The results of the training and testing of the proposed models reveal that the constructed VREMs are statistically comparable. In particular, the Vol2Vol approach has a minimum L1 loss of 0.01, which further decreases to 0.0084 as the line-of-sight (LoS) probability increases to 0.95.

A CFO-Assisted Algorithm for Wireless Time-Difference-of-Arrival Localization Networks: Analytical Study and Experimental Results

Localization of wireless transmitters is traditionally done using Radio Frequency (RF) sensors that measure the propagation delays between the transmitter and a set of anchor receivers. One of the major challenges of wireless localization systems is the need for anchor nodes to be time-synchronized to achieve accurate localization of a target node. Using a reference transmitter is an efficient way to synchronize the anchor nodes Over-The-Air (OTA), but such algorithms require multiple periodic messages to achieve tight synchronization. In this paper, we propose a new synchronization method that only requires a single message from a reference transmitter. The main idea is to use the Carrier Frequency Offset (CFO) from the reference node, alongside the Time of Arrival (ToA) of the reference node messages, to achieve tight synchronization. The ToA allows the anchor nodes to compensate for their absolute time offset, and the CFO allows the anchor nodes to compensate for their local oscillator drift. Additionally, using the CFO of the messages sent by the reference nodes and the target nodes also allow us to estimate the speed of the targets. The error of the proposed algorithm is derived analytically and is validated through controlled laboratory experiments. Finally, the algorithm is validated by realistic outdoor vehicular measurements with a software-defined radio testbed.

Joint Target and Transmitter Localization With AOA and DTD Using Passive Sensors

Joint Target and Transmitter Localization With AOA and DTD Using Passive Sensors

Not all sanctuaries are created equal: variation in protected area selection by wintering mallards

AbstractPublic lands managed for wildlife frequently provide various forms of sanctuary to increase residency times and allow access to energetic and other habitat resources for waterfowl. The influence of sanctuary type and disturbance regime on resource use and fine‐scale movements of waterfowl has not been investigated extensively using currently available transmitter technologies. We examined mallard (Anas platyrhynchos) use of various types of waterfowl sanctuary and non‐sanctuary areas in the Mississippi Alluvial Valley region of eastern Arkansas, USA, during winters of 2019–2021. We deployed 105 global positioning system transmitters on mallards at 4 closed‐access spatial sanctuaries on or adjacent to Dale Bumpers White River National Wildlife Refuge. We used hourly transmitter locations to examine mallard use of public sanctuary areas, public hunt areas, and private lands using integrated step selection analysis. Public sanctuary areas provided varying levels of protected status, public hunt areas allowed for varying levels of hunting intensity by duck hunters, and private lands were open to waterfowl hunting and other forms of private uses but may or may not have been hunted at any specific frequency. Mallards selected spatial sanctuary and avoided public hunt areas, other sanctuary types, and private lands during the day. In contrast, mallards selected for private lands over spatial sanctuary at night. Mallards tended to avoid areas that allowed duck hunting or used them during the night when risk of harvest mortality was removed. After the hunting season closed, mallards began using areas that previously allowed duck hunting during the day, suggesting that risk was the primary factor influencing site use. Moreover, mallards were 1.6 times more likely to use public daily hunt areas and 2.1 times more likely to use private lands potentially open to hunting during the day than spatial sanctuary 2 weeks after the close of duck hunting season in February. Spatial sanctuaries appear more effective in influencing mallard use than temporal sanctuaries or inviolate sanctuaries, which are commonly used by state and federal agencies. Partial daily, daily, or activity‐specific (e.g., no hunting past noon, no hunting 3 days/week, no waterfowl hunting) closures to encourage mallard use of temporal sanctuaries do not appear to reduce the perceived harvest‐related risk to mallards enough for them to view these areas as accessible or significantly increase their use.

Multiple attributes based physical layer authentication through propagation scenario identification in the internet of vehicles

The Internet of Vehicles (IoV) systems improve road safety through coordination among vehicles, but they are vulnerable to impersonation attack due to their broadcast communication nature. Existing cryptographic authentication approaches are complex for resource-limited IoV devices, which motivated the development of less complex Physical Layer Authentication (PLA) approaches. However, the existing PLA approaches use all channel attributes for all communication scenarios and do not update reference attributes used to authenticate newly observed attributes during authentication, which leads to poor performance when the attributes become insensitive to communication scenarios. To address these limitations, we propose a multiple attributes-based PLA scheme that flexibly selects effective attributes according to current communication scenarios for improved authentication. First, the historical channel attributes including the ricean K-Factor (KF), the Delay Spread (DS), and the Azimuth Spread of Arrival (ASA) together with the communication scenarios (i.e., Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS)) information of transmitters are used to establish a relationship and train a Long Short-Term Memory (LSTM) neural network to identify current communication scenarios. The softmax function is then used to evaluate the scenario identification performance of each attribute and the attributes with maximum softmax values are dynamically selected for authentication. Second, to track and update the reference attributes, we use the historical attributes together with the past location and communication scenarios information of transmitters and train our model to predict future attributes for authentication. Finally, we use the Euclidean distance to measure the similarity between the selected and the predicted attributes to authenticate the transmitters. We validate the effectiveness of our approach and compare its performance with the existing methods through extensive simulations using realistic radio channel characteristics generated from the Quasi Deterministic Radio channel Generator (QuaDRiGa) platform. The results of the evaluation show that our system improves authentication performance and its false alarm and miss detection are respectively 19.19% and 52% lower than the existing approaches.

Robust Time-of-Arrival Location Estimation Algorithms for Wildlife Tracking

Time-of-arrival transmitter localization systems, which use measurements from an array of sensors to estimate the location of a radio or acoustic emitter, are now widely used for tracking wildlife. Outlier measurements can severely corrupt estimated locations. This article describes a new suite of location estimation algorithms for such systems. The new algorithms detect and discard outlier time-of-arrival observations, which can be caused by non-line-of-sight propagation, radio interference, clock glitches, or an overestimation of the signal-to-noise ratio. The new algorithms also detect cases in which two locations are equally consistent with measurements and can usually select the correct one. The new algorithms can also infer approximate altitude information from a digital elevation map to improve location estimates close to one of the sensors. Finally, the new algorithms approximate the covariance matrix of location estimates in a simpler and more reliable way than the baseline algorithm. Extensive testing on real-world data involving mobile transmitters attached to wild animals demonstrates the efficacy of the new algorithms. Performance testing also shows that the new algorithms are fast and that they can easily cope with high-throughput real-time loads.

Linear-in-dB Logarithmic Signal Strength Sensor Circuit for Wireless Power Transfer Receivers

Tracking systems for wireless power transfer are becoming a necessity. The received target signals are sometimes weak, which is why it is critical to have a dedicated received–signal–strength indicator (RSSI) for signal detection. It can also be used for transmitter localization and automatic gain control (AGC) to ensure continuous coverage. In this paper, a logarithmic detector coupled with a high-speed full-wave rectifier is designed for an RSSI system. The aim is to place the RSSI on the receiver side of unmanned aerial vehicles used, for example, as agricultural drones. This system is intended to operate in a scenario where multiple drones in a smart farm are charged wirelessly whilst airborne. An RSSI is placed in the receiver to detect and track wireless signals and to ensure the drone is charged while in motion. The RSSI system operates at 5.8 GHz, reported for the first time, and is capable of detecting signal strengths from −60 dBm to 0 dBm with a sensitivity level of 17 mV/dBm. A logarithmic error of 0.4 dB with a dynamic range of 34 dB was achieved. The proposed RSSI system was designed using 65 nm TSMC CMOS technology, and it exhibits high sensitivity, better efficiency, and lower power consumption than those in other reported works.

Optimized Allocation of Fault Transmitters for Improving the Recloser-Fuse Coordination

New technologies are currently delivering protective devices that can enhance the design of protection coordination in modern distribution systems. This paper proposes an integer linear programming (IP) model to find the optimal location of a Fault Transmitter (FT) in the distribution system. The proposed IP model provides the optimal FT allocation considering the minimization of sustained interruption frequency and momentary interruption duration. A study of the appropriate number of FT reveals the best alternative in the proposed adaptive protection scheme. A Monte Carlo simulation with a richer representation of stochastic fault scenarios is used for further evaluation of the solutions obtained with the proposed IP model. The combined strategies allow full-scale studies of the optimal allocation of FT in distribution systems. The results of studies using two test systems certify the benefits of the proposed strategy in fuse-saving coordination and in reducing the total duration of the momentary interruption, which can cause power quality problems.

An Evaluation of the Signal to Noise Ratio (SNR) of Next Generation Wireless Communication Systems using Large Intelligent Surfaces: Deep Learning Approach

The existing data rate must be greatly increased in order to support the numerous applications of the next generation communication systems. Using Large Intelligent Surfaces (LIS), which are a panel with mounted reflective components, is one way to address this problem. Their primary function is to divert the electromagnetic signal to the intended user. As a result, the received signal's strength and reception quality both improve, improving the Quality of Service (QoS). Machine Learning algorithms have been used to implement LIS in a number of ways, including channel estimation and the calculation of phase shifts (discrete), to mention a few. Here, it is suggested that the Signal to Noise Ratio (SNR) for a LIS-supported communication system be assessed using a Deep Learning (DL) model. On the DL model, the effects of the SGD, Adam RMSProp Adadelta and Adamax optimizers are investigated. The Mean Squared Error (MSE) loss function is taken into account. The Adam optimizer offers the highest level of precision, making it preferable to other optimizers. The SNR for 10 users is measured using the suggested DL model with Adam Optimizer. The outcomes of this work is contrasted with the SNR estimate by an existing technique that calculates SNR based on LIS size and the location of transmitter and receiver with an accuracy of 93.5%. The simulation results indicate that the accuracy is increased to 96% using the suggested DL model which attains an average error of 3.6.

Soil Monitoring for Agriculture Activity using Low Power Wide Area Network

The new Covid-19 pandemic is affecting many economic sectors, such as the agricultural industry. The farmers faced many challenging constraints during the pandemic due to the enforcement of the Movement Control Order (MCO), resulting the unproductive crops. This paper proposed smart agriculture adopted with the Internet of Things (IoT) and Low Power Wide Area Networks (LPWAN) technology to be one of the possible solutions for the problems. Early precautions will minimize unproductive crops by monitoring the soil for healthy crops by monitoring the moisture and the pH level of the soil in real time. In this paper, a soil monitoring system using (Long-range) LoRa technology as one of the solutions for LPWAN to monitor the soil for agriculture has been set up. The soil moisture and pH sensors used to monitor the condition of the soil. The monitoring system uses LoRa as the communication network for the two sensors integrated into the Arduino Uno microcontroller as the transmitter and a receiver to monitor the status of the soil. This project enables the user to monitor the soil conditions and control the watering of the crops. The result concludes that this system shows 100% accuracy of the packet data received by the receiver, where the location of transmitter to the receiver was 1.4 meters apart.

Влияние осциллирующего поляризационного тока на поглощение ОНЧ-радиоволн, распространяющихся вдоль линии геомагнитного поля

Taking into account the oscillating polarization current in Maxwell's equations made it possible to determine the “transparency band” of the ionosphere by the angle between the direction of the magnetic field and the wave vector for VLF radio waves. Waves with both linear polarization of the electric field vector and an extraordinary wave can penetrate to heights above the maximum of the ionosphere layer. The absorption of radio waves is largely determined by the magnitude of the angle between the geomagnetic field vector and the direction of propagation of the radio wave. When radio waves propagate strictly along the geomagnetic field line, an extraordinary wave is not absorbed, but a wave with linear polarization is absorbed. Calculations make it possible to explain the maxima of the energy spectrum of radio waves recorded by the DEMETER satellite in the area of the transmitter location and in the area of the magnetically conjugate point.

Physical layer authentication in the internet of vehicles through multiple vehicle-based physical attributes prediction

The high security and low complexity of physical layer authentication (PLA) make it a promising complement for complex cryptographic authentication approaches, especially for Internet of Vehicles (IoV) systems constrained by limited computational capabilities and facing increasing security threats due to the broadcast nature of their communications. However, existing PLA schemes exploiting geographical location information to predict channel characteristics for authentication face the challenge of location falsifying/spoofing attacks. Moreover, they either require cryptographic-based initial authentication or cannot be applied to moving vehicle scenarios. To tackle these challenges, we propose a PLA scheme based on the Gaussian process (GP) regression that jointly considers the location and speed attributes of vehicles to enhance the reliability of authentication in the IoV network. Specifically, the historical channel state information attributes together with the location and speed information of transmitters are utilized to establish a mapping and train a GP model to predict the next legitimate location and speed of a transmitter for authentication. First, for vehicle-to-road side unit (RSU) authentication, the trained GP model is stored on RSU and used to authenticate vehicles entering its vicinity by cross-verifying their reported location and speed information with the ones predicted by the model. Next, for vehicle-to-vehicle authentication, we propose an RSU-assisted authentication, where the RSU in the location shared by two vehicles is used to assist in verifying the validity of their reported location and speed information. Finally, for RSU-to-vehicle authentication, we leverage the path loss and angle of arrival of a signal from RSU to estimate and cross-verify its location. We utilize QuaDRiGa, a quasideterministic radio channel generator to generate realistic channels for experimental validations. The results of simulation tests conducted demonstrated that our approach significantly improves authentication performance compared with the existing approaches.

Lagrange programming neural network for robust passive elliptic positioning

This contribution studies passive elliptic positioning (PEP) with unknown transmitter locations, a localization technique having great potential applicability ranging from underwater wireless sensor networks to intelligent transportation systems. Specifically, we aim to address the challenge of employing PEP in complex real-world environments where outliers may exist, by using the concept of robust statistics. To achieve such a goal, we replace the ℓ2 loss in the traditional nonlinear least squares formulation by a differentiable cost function that possesses outlier-resistance. The neurodynamic approach of Lagrange programming neural network is then adopted to solve the resultant nonconvex statistically robustified PEP problem in a computationally efficient manner. Simulations and acoustic positioning experiments demonstrate the performance superiority of our proposal over its competitors.

UAV-Aided Radio Map Construction Exploiting Environment Semantics

This paper constructs a full dimensional (6D) radio map to predict the channel gain between any transmitter location and any receiver location based on received signal strength (RSS) measurements between low-altitude aerial nodes and ground nodes. The main challenge is how to describe the signal strength attenuation due to the blockage from the environment. Conventional interpolation-type approaches fail to exploit the close relation between the radio map and the geometry of the environment. This paper proposes to construct radio maps by first estimating and constructing a multi-class 3D virtual obstacle map that describes the geometry of the environment with radio semantics. Mathematically, a least-squares problem is formulated to jointly estimate the virtual obstacle map and the propagation parameters. This problem is found to have a partial quasiconvexity that leads to the development of an efficient parameter estimation and radio map construction algorithm. Numerical experiments confirm that the proposed method substantially reduces the amount of measurement required for achieving the same radio map accuracy. It is also demonstrated that in a unmanned aerial vehicle (UAV)-aided relay communication scenario, a radio map assisted approach for UAV placement can achieve more than 50% capacity gain.