Wireless Channel Model Research Articles

Wireless Channel Propagation Model for Inland Waterway Bridge Scenario

Intelligent shipping is a crucial part of the transportation system, while inland river intelligent shipping is a major safeguard of intelligent transportation. Compared with the studies of mobile fading channels in land-based environments, less current research has focused on channel measurements and modeling for inland waterway bridge environments. In this paper, a segmenting radio channel model is proposed for inland highway and railway combined bridges. The ship's path under the bridge was divided into three phases, and the attenuation of signal strength was modelled separately for each. Hence, it shows ship-to-ship wireless channels in different areas and path loss on inland navigation bridges. A segmented model, instead of a basic path loss model, can accurately forecast path loss and provide a practical approach in ship-to-ship wireless channel transmission scenarios over bridges. Consequently, the channel measurements and modeling in the typical inland waterway are of great significance for establishing a reliable inland navigation broadband radio communication system.

Research on wireless channel model based on improved ray tracing algorithm that considers multiple diffuse scattering and employs pyramid-shaped ray tubes as the carrier

This paper extensively utilizes fine three dimensional environmental data obtained from laser point clouds. Based on theories such as geometrical optics and effective roughness theory, a deterministic wireless channel model is established, which integrates higher-order diffuse scattering. This model is referred to as the ray tracing fusion with higher-order diffuse scattering model. To expedite the collision calculation between rays and the scene, this paper introduces a combined approach of voxelization and signed distance field, resulting in a remarkable 16-fold improvement in computational speed. Moreover, aiming to balance accuracy and efficiency, the paper systematically analyzes the optimization computation parameters of the model. Finally, the proposed model is validated using measurement data in the frequency range of 1 GHz to 6 GHz in mountainous terrain. The results indicate that the predicted outcomes of the proposed model have an accuracy within 6 dB compared to the measurement results, and are superior to ITU-R P.1546, which is an international standard recommended by the International Telecommunication Union for modeling electromagnetic wave propagation in undulating terrain. This provides necessary technical support for network planning and optimization.

Composite underwater optical wireless channel modeling based on Electric field Monte Carlo method

Composite underwater optical wireless channel modeling based on Electric field Monte Carlo method

Multi-Cluster Approaches to Radio Sensor Array Channel Modeling and Simulation.

In this paper, we explore the physical propagation environment of radio waves by describing it in terms of distant scattering clusters. Each cluster consists of numerous scattering objects that may exhibit certain statistical properties. By utilizing geometry-based methods, we can study the channel second-order statistics (CSOS), where each distant scattering cluster corresponds to a CSOS, contributes a portion to the Doppler spectrum, and is associated with a state-space multiple-input and multiple-output (MIMO) radio channel model. Consequently, the physical propagation environment of radio waves can be modeled by summing multiple state-space MIMO radio channel models. This approach offers three key advantages: simplicity, the ability to construct the entire Doppler power spectrum from multiple uncorrelated distant scattering clusters, and the capability to obtain the channels contributed by these clusters by summing the individual channels. This methodology enables the reconstruction of the radio wave propagation environment in a simulated manner and is crucial for developing massive MIMO channel models.

Localization algorithm of soil moisture monitoring in irrigation area based on weighted correction of distance measurement

At present, Wireless Sensor Network technology are widely used in the fields of agricultural environment monitoring. Whether we use ground network or ground-air coordination, the position coordinates of unknown nodes are an important feature of sensor information collection. To achieve the goal of a wireless monitoring system for field soil irrigation and its node positioning, we proposed an RSSI (Received Signal Intensity Indication)-based agricultural environment irrigation monitoring system. The algorithm has three stages: RSSI ranging, error correction, and localization. In the RSSI ranging phase, we obtain inter-node measurement distance by wireless channel modeling. In the distance-weighted correction phase, considering the difference between the measured distance of the beacon nodes and the actual distance, we analyze and select the relative error coefficient for the beacon nodes to correct the measured distance between the unknown nodes and the beacon nodes within their communication range. In the positioning stage for the nodes to be located, we estimated the required node coordinates using a weighted centroid localization method. In addition, by analyzing the monitoring data, we know the changes in soil moisture in real-time, which provides new ideas for irrigation automation and the efficient utilization of water resources. When designing the algorithm, we thoroughly considered the influence of RSSI ranging inaccuracy and the quantity of beacon nodes on the localization precision. The test demonstrated that the method presented in this paper has higher localization precision and lower computation cost.

Closed-form energy-based signal detection analysis in presence of a Lomax fading channel in full hyper-Rayleigh regime

The presented research examines the energy-based spectrum sensing problem of a signal transmitted over a wireless communication channel with severe fading. To account for the most challenging propagation conditions, a recently developed Lomax wireless channel model was utilized, which exhibits hyper-Rayleigh characteristics over the entire range of possible parameter values. For the channel model under consideration, the existence of the specific hyper-Rayleigh regimes (i.e., weak, strong, and full) are identified asymptotically and studied numerically for a finite signal-to-noise ratio. For the most severe fading conditions, the system’s performance was assessed in terms of the average probability of detection, receiver operating characteristics, and area under the curve. These metrics were derived in closed-form for the cases of Maximum Ratio Combining at the receiver and without diversity reception. The obtained expressions were analyzed via numerical and statistical simulation as functions of system and channel parameters, including the sensing base, average signal-to-noise ratio, and scale parameter for the signal-to-noise probability distribution. The results from numerical simulations were compared with existing regulations specified in the 5G standard for energy-based detection in medium access procedures.

On the Tacit Linearity Assumption in Common Cascaded Models of RIS-Parametrized Wireless Channels

On the Tacit Linearity Assumption in Common Cascaded Models of RIS-Parametrized Wireless Channels

Wireless communication channel modeling based on ML

Nowadays, wireless communication plays an important role in various fields of our lives, and in order to ensure good communication performance, reliable channel models are important. In this paper, we present a comprehensive survey of Machine Learning (ML) based channel modeling methods to help with the estimation and prediction accuracy on wireless channel modeling. To begin with, we start with a review of the traditional methods of wireless communication channel modeling, basically empirical method and deterministic method. Then, we introduce several ML methods to address limitations of traditional ways will be used for channel modeling, such as Support Vector Machine (SVM), Random Forests, autoencoder, Deep Learning, etc. Lastly, we demonstrate the application of ML methods including deep learning, SVM, and random forests on wireless channel modeling. Through learning the underlying channel distribution, Deep Learning methods have already demonstrated remarkable performance in channel modeling for different scenarios in some previous learning. Also, by training with suitable kernels and conducting sufficient training iterations, optimal hyperplanes can be identified, Support Vector Machines (SVM) can then be utilized to predict channel characteristics based on input features. Random Forests can identify the most relevant features influencing the channel and optimize system design by handling complex and high-dimensional data through iterative feature selection and splitting criteria. These methods achieve competitive accuracy with respect to traditional methods, however, there are still challenges and issues to be addressed in the application of machine learning methods for communication channel modeling.

Spatio-temporal adaptive algorithm using Hamming code based on 3D-MIMO wireless channel model

Space-time coding is an effective method for constructing communication systems while saving bandwidth, taking into account the shortage of frequency resources and power in fading channels, which is implemented based on the advantages of using adaptive antenna arrays. Currently, WiMAX radio access technology has become widespread; its use is justified in conditions of direct visibility between the base and mobile stations, but in a city with dense buildings and in the presence of organized interference, its use leads to an increase in the likelihood of bit errors. At the same time, the combination of OFDM (orthogonal frequency division multiplexing) and MIMO (spatial coding) technologies based on adaptive antenna arrays significantly reduces the probability of bit errors, and therefore increases the throughput of the communication system. The use of adaptive space-time devices at the outputs of antenna arrays makes it possible to select a signal path with maximum power and with a minimum error in receiving OFDM pilot subcarriers, even in the absence of direct visibility between the base and mobile stations and in conditions of active interference. In addition, the use of a 3D model of the communication channel will allow us to evaluate the effectiveness of the communication system model in real operating conditions. The goal of the work is to solve the problem of additionally increasing noise immunity in communication systems in conditions of numerous reflections caused by dense buildings in urban environments, using a combination of Hamming code, OFDM and an algorithm for adaptive spatio-temporal signal processing in receivers such as SISO (Singl Input – Singl Output), and MIMO (Multiply Input – Multiply Output), built on adaptive antenna arrays. The work calculated the weight coefficients of the adaptation algorithm using the theory of eigenvalues and eigenvectors of the spatial correlation matrix of signals at the outputs of the adaptive antenna arrays. Modeling of algorithms and a 3D model of the communication channel in Matlab using the Hamming code was carried out, as a result, an increase in the efficiency of error correction for the Hamming code was demonstrated, as well as a significant reduction in the probability of code errors and an increase in the performance of the communication system when using an adaptive algorithm. At the same time, the article uses the term “multipath” channel in contrast to the generally accepted, but incorrect “multibeam” channel, since multibeam can be a characteristic of an antenna system. The presented results confirm that when processing signals in a receiving system in a multipath channel with dense urban development, the use of adaptive spatiotemporal algorithms provides increased noise immunity and radio communication capacity, especially for image transmission when using unmanned aerial vehicles.

Performance of Weighted Random Reference Patterns on Wireless Channel Model for Gesture Recognition

Performance of Weighted Random Reference Patterns on Wireless Channel Model for Gesture Recognition

Enhancing longevity: Sustainable channel modeling for wireless-powered implantable BANs

Wireless body area network (WBAN) has revolutionized the healthcare sector by enabling remote monitoring and control of wearable and implantable devices, providing freedom of mobility to patients. However, wireless channel modeling in BAN is a crucial aspect for designing an efficient off-body, on-body and in-body communication. Due to the unique characteristics of the human body, it aims to characterize the signal propagation through skin, tissues, internal organs and biological fluids of a patient’s body. Moreover, it is important to enhance the battery life of the low-powered devices for a sustainable BAN. In this work, we provide a hybrid communication channel model for wireless power transfer in a BAN including both off-body and in-body communication channels. An indoor room scenario is considered in which a movable patient having an implant inside its body is present along with an RF power source (for example, a Wi-Fi access point) situated in a ceiling corner. Implant is assumed to inhibit energy harvesting capability. For practicability, we have considered the effect of path loss, partition walls, floor attenuation factor along with other important body parameters. Specifically, we aim to statistically characterize this hybrid communication system, for which unique closed-form expressions of the probability distribution functions of the received power have been derived, thereby first calculating the instantaneous power at different layers of human body and then obtaining the closed-form expression for average received power. All the derived mathematical expressions have been verified via numerical simulations. Further, for elongating the lifespan of implants, we investigated the average power harvested by an implant and its power outage probability for analyzing the sustainability of implants. The results are numerically validated, considering different types of indoor room scenarios, in addition to providing key design insights.

A new derivation of the Nakagami-m distribution as a composite of the Rayleigh distribution

Mobile communications systems are affected by what is known as fading, which is a well-known problem largely studied for decades. The direct consequence of fading is the complete loss of signal (or a large decrease of the received power). Rayleigh fading is a reasonable model for wireless channels although, Nakagami-m distribution seems better suited to fitting experimental data. In this paper we obtain the Nakagami-m distribution as a composite (mixture) of the Rayleigh distribution, a result which as far as we know it has not been shown in the literature. This representation of the Nakagami-m distribution facilitates computations of the average BER (Bit Error Rate) for DPSK (Differential Phase Shift Keying) and MSK (Minimum-Shift Keying) modulations for this distribution and higher moments of them, which is of great applicability to modeling wireless fading channels. Furthermore, a simple, not depending on any special function, apart of the Gamma function, bivariate version of the Nakagami-m distribution is also proposed as a special case of the multivariate version which is also presented. The proposed composite distribution is simulated through the standard procedure of summation of phasors, and results for the new closed-form measures for the MSK modulation are also shown. From that it is clear that the alternative formulation of the Nakagami-m distribution allows for easier modeling of fading fading-shadowing wireless channels through the new explicit second order statistics metrics. is well suited for modelling fading-shadowing wireless channels.

Wireless Channel Models for Marine Communication

Wireless Channel Models for Marine Communication

Modeling and oblique transmission characteristics of an underwater wireless optical communication channel based on ocean depth layering.

Underwater wireless optical communication is widely considered in the field of underwater communication due to its high bandwidth and low latency. In a real transmission link, the temperature and salinity of seawater, chlorophyll concentration, and bubble density vary with ocean depth. Therefore, the depth of the optical transmitter in seawater and the tilt angle of the beam will exhibit different beam transmission characteristics. In this paper, an underwater oblique-range layered channel model considering the combined effects of dynamic turbulence, absorption, and scattering is developed based on real data of seawater at different depths measured by the Global Ocean Observing Buoy Argo and the Woods Hole Oceanographic Institution BCO-DMO. The effects of transmission distance, transmitter tilt angle, and transmitter depth on the oblique-range transmission characteristics of the beam in seawater are discussed. The simulation results show that, at the same transmission distance, the beam centroid displacement increases with an increase in transmitter depth only when the transmitter is located above the interior of the thermocline. When the transmitter is located below the interior of the thermocline, the influence of the transmitter tilt angle on the beam centroid displacement decreases. This indicates that at different depths within the interior of the thermocline, the optical beam transmission characteristics exhibit significant variations.

Wireless Channel Model Based on Ray Tracing Algorithm in the Sea Environment

The maritime environment is complex and changeable, and an accurate maritime wireless channel model is particularly difficult to establish. This study investigates the sensitivity of a three-dimensional (3D) maritime ray tracing (RT) model for the use of the geometrical optics in radio channel characterizations of maritime environments. The channel measurement experiments are mainly carried out at three frequency points of 1.5, 1.8, and 2.5 GHz, and the simulation results of the model are compared and corrected with the measured received power. Analysis shows that the modified model simulation results at 1.5, 1.8, and 2.5 GHz frequency points are in good agreement with the actual measurement, and the RMS is within 7 dB. Moreover, the reverse RT algorithm has the advantages of fast calculation speed and high efficiency. The effects of the different sea conditions, frequencies, and distances on the propagation of electromagnetic waves are analyzed on the basis of this model.

Geometry-based Non-Stationary Inter-large-satellite Wireless Channel Model

Geometry-based Non-Stationary Inter-large-satellite Wireless Channel Model

Blockage loss and shadow fading behavior of millimeter‐wave signals due to human bodies at 28 GHz

AbstractAs the millimeter‐wave (mm‐Wave) spectrum is considered to be an essential enabler to the fifth generation (5G) wireless communication systems. Human movements are one of the most significant factors that cause transient blockage in indoor mm‐wave channels. In this letter, human blockage measurements and shadow fading statistics due to human body movements in an indoor office environment are reported for the 28 GHz band. The effect of human bodies on the channel is measured for several scenarios including a variety of population and using diverse antenna heights. The reported shadow fading statistics include both the duration and the depth of the blockage fade, and accordingly, we propose several empirical models that cater for such blockage events. The findings reported in this letter could improve the modeling of indoor radio channels at 28 GHz bands by considering the presence of humans, as well as their movements.

Frequency Domain UWB Channel Characterization and Modeling in Indoor Static Environment

In this article, distance and frequency‐dependent indoor wireless channel characterization and modeling are presented in C, X, and Ku bands at the line of sight (LOS) and nonline of sight (nLOS) scenarios. Complex frequency responses of the indoor radio channel are measured using the frequency‐domain approach. The terminal and measurement setup dependencies of the propagation channel are deconvoluted using closed‐form expressions devised from a two‐port network model approach of the antenna transreceiver system. Subsequently, terminal independent frequency‐domain channel transfer functions (CTFs) and time‐domain channel impulse responses (CIRs) are derived for accurate statistical analysis of large‐ and small‐scale fading parameters of the propagation channel. A distance‐dependent statistical pathloss model is proposed. Successively, a frequency domain channel model using the fifth‐order autoregressive process is proposed. Subsequently, the existence of the multiple clusters in the environment is analyzed by the poles of the transfer functions of the model. Models are validated in both time and frequency domains by comparing the computer‐simulated synthetic model data with the empirical channel responses. The measurement data and the model data are seen to be in good agreement.

The Lomax Distribution for Wireless Channel Modeling: Theory and Applications

The Lomax Distribution for Wireless Channel Modeling: Theory and Applications

Physics-informed Generalizable Wireless Channel Modeling with Segmentation and Deep Learning: Fundamentals, Methodologies, and Challenges

Physics-informed Generalizable Wireless Channel Modeling with Segmentation and Deep Learning: Fundamentals, Methodologies, and Challenges