Wireless Channel Characteristics Research Articles

Deep Learning-Based Optimization for Maritime Relay Networks

The complexity and uncertainty of the marine environment pose significant challenges to the stability and coverage of communication links. Due to the limited coverage range of traditional onshore base stations (BSs) in marine environments, relay technology has become an essential approach to extending communication coverage. However, the rapid variations in marine wireless channels and the complexity of hydrological conditions make it extremely difficult to obtain accurate channel state information (CSI). In particular, dynamic environmental factors such as waves, tides and wind speed cause channel parameters to fluctuate significantly over time. To address these challenges, this paper proposes a cooperative communication strategy based on ships and designs a novel channel modeling method to accurately capture the characteristics of marine wireless channels. Furthermore, a deep learning-based optimization scheme is proposed, which formulates the relay selection problem as a spatiotemporal classification task. By integrating the spatial positions of candidate relays and their communication conditions, the proposed scheme enables real-time selection of the optimal relay while evaluating link connectivity probabilities under hydrological influences. Simulation results confirm that the proposed method achieves high accuracy even in rapidly changing marine environments.

Multifrequency Wireless Channel Measurements and Characterization in Indoor Industrial Scenario

Multifrequency Wireless Channel Measurements and Characterization in Indoor Industrial Scenario

A Breast Tumor Monitoring Vest with Flexible UWB Antennas-A Proof-of-Concept Study Using Realistic Breast Phantoms.

Breast cancers can appear and progress rapidly, necessitating more frequent monitoring outside of hospital settings to significantly reduce mortality rates. Recently, there has been considerable interest in developing techniques for portable, user-friendly, and low-cost breast tumor monitoring applications, enabling frequent and cost-efficient examinations. Microwave technique-based breast cancer detection, which is based on differential dielectric properties of malignant and healthy tissues, is regarded as a promising solution for cost-effective breast tumor monitoring. This paper presents the development process of the first proof-of-concept of a breast tumor monitoring vest which is based on the microwave technique. Two unique vests are designed and evaluated on realistic 3D human tissue phantoms having different breast density types. Additionally, the measured results are verified using simulations carried out on anatomically realistic voxel models of the electromagnetic simulations. The radio channel characteristics are evaluated and analyzed between the antennas embedded in the vest in tumor cases and reference cases. Both measurements and simulation results show that the proposed vest can detect tumors even if only 1 cm in diameter. Additionally, simulation results show detectability with 0.5 cm tumors. It is observed that the detectability of breast tumors depends on the frequency, antenna selection, size of the tumors, and breast types, causing differences of 0.5-30 dB in channel responses between the tumorous and reference cases. Due to simplicity and cost-efficiency, the proposed channel analysis-based breast monitoring vests can be used for breast health checks in smaller healthcare centers and for user-friendly home monitoring which can prove beneficial in rural areas and developing countries.

142 GHz Sub-Terahertz Radio Propagation Measurements and Channel Characterization in Factory Buildings

142 GHz Sub-Terahertz Radio Propagation Measurements and Channel Characterization in Factory Buildings

6G and Beyond Wireless Channel Characteristics for Physical Layer Security: Opportunities and Challenges

6G and Beyond Wireless Channel Characteristics for Physical Layer Security: Opportunities and Challenges

Wireless Channel Characterization From 2 to 28 GHz in an Outdoor Parking Lot

Wireless Channel Characterization From 2 to 28 GHz in an Outdoor Parking Lot

Three-Dimensional Ray-Tracing-Based Propagation Prediction Model for Macrocellular Environment at Sub-6 GHz Frequencies

This paper presents a 3D ray-tracing model using geometrical optics and the uniform theory of diffraction in radio channel characterizations of macrocellular environments. On the basis of the environmental information obtained from a digitized map, the model is effectively applied. A technique considering multiple reflections and diffractions through the ray path classification is utilized in this model. Ray paths belonging to each ray category are determined using different methods. The proposed model is justified (the prediction accuracy of the model is better than 6.5 dB) with measurement data for the two scenarios and can provide reliable theory as a basis for radio wave propagation prediction and network planning in urban macrocellular environments.

Две методики измерения aмплитудно-частотных характеристик многолучевых ионосферных коротковолновых радиолиний по данным наклонного радиозондирования ионосферы

Two methods of measuring the amplitude-frequency characteristics of multipath ionospheric shortwave radio channels with high frequency resolution (tens-hundreds of hertz) are presented. Since traditional narrow-band channels do not allow to separate the various modes of ionospheric propagation of short waves interfering with each other, it is proposed in this paper to use broadband signals, which makes it possible to separate the received modes of ionospheric propagation of a radio signal, as well as to distinguish the signal from interference. The advantage of the first technique is that, together with the amplitude-frequency characteristic of the radio line, as a result, the mode pattern of multipath propagation of short waves becomes known, which allows us to draw qualitative conclusions about the propagation of radio waves, as well as to apply classifications according to empirical multipath models. Disadvantages of the first technique: the manual stage of the selection of mode tracks on the ionograms of oblique sensing, as well as the loss of some information about the energy of the mode tracks due to the diffuseness (blurring) of the traces of mode tracks on the ionogram. The second method, on the contrary, gives only an estimate of the amplitude-frequency characteristics of a multipath shortwave radio line without revealing the mode pattern of multipath, but does it automatically and without losing information about the energy of diffuse tracks. The presentation of both methods in one article allows you to indicate the advantages and disadvantages of each of them, and, accordingly, the preferred areas of application. Since it is important to have a set of techniques with an integrated system approach to the study and measurement of the characteristics of the ionosphere and ionospheric radio channels.

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.

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.

Indoor wireless channel characterization in the W band for the planning of 6G communication systems

This work presents a wireless channel characterization based on an extensive campaign of massive multiple-input multiple-output (mMIMO) measurements conducted under line-of-sight (LOS) conditions. These measurements focus on the 94 GHz band (W band), and the results are compared to the same positions measured at 60 GHz. The values of the relative received power, root mean square (RMS) delay spread, maximum excess delay (MED), and coherence bandwidth in an indoor laboratory environment are analyzed. The results of this study can contribute to a better understanding of the propagation channel behavior in the 94 GHz band, and can be used to assess the potential of this band for the upcoming sixth-generation (6G) networks in laboratory environments.

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.

Multi-Frequency Channel Measurement and Characteristic Analysis in Forested Scenario for Emergency Rescue

Wireless communication has been widely used in emergency rescue, including from command vehicles to command vehicles, command vehicles to rescue teams, command vehicles to wireless sensors, and rescue teams to intelligent platforms such as unmanned vehicles. Compared to those used in cities and suburbs, when the same communication equipment is used in forests, its communication performance, such as transmission distance, is entirely different. The main reason for this phenomenon is the extraordinary complexity of wireless signal propagation in forest scenarios. Therefore, in order to accurately and quantitatively describe the wireless channel characteristics in forest scenarios, a frequency channel measurement activity is conducted in a forest and analysis is performed to acquire the channel characteristics in forest scenarios. The measurements are carried out at 380 MHz, 640 MHz, and 1420 MHz in virgin forest. Based on the measurement data, the average power delay profile (APDP) is obtained, and multipath components (MPCs) are extracted. Root mean square (RMS) delay spread and path loss (PL) are analyzed according to MPCs. Furthermore, a new path loss model is proposed. Finally, a new path loss model and relative analysis are provided.

Energy Efficient Routing Technique Using Enthalpy Ant Net Routing for Zone-Based MANETS

Routes discovery that can provide reliable data transmission in Mobile Ad-hoc Networks is challenging due to its wireless channel characteristics and dynamic transmission environment. Ad-hoc networks frequently experience link failure because nodes are mobile and their positions are not fixed. The cellular ad hoc community's dynamic nature makes it possible to analyze multi-route routing protocols specifically. One of the most trustworthy and environment-friendly routing methods is the Zone Routing Protocol used in Mobile Ad Hoc Networks. Offering timely and trustworthy communication services, however, depends critically on maintaining the Quality of Service, electricity performance, and outstanding resource management. In this research, we suggested an energy-efficient routing method for zone-based MANETs called enthalpy and net routing. First, a completely fuzzy quarter clustering based on an Energy guide is used to perform the clustering. With the help of fuzzy memberships, the AFCM set of rules permits the input of statistics for each elegance. In the second, the best route is selected using Enthalpy Ant Net Routing (EANR) while taking the following factors into account: community disconnection, channel error, buffer overflow, and contention at the link layer. The results of the experiment show that the collection of rules performs better than many existing algorithms in terms of community longevity, electricity consumption, and other metrics.

A Deep Convolutional Autoencoder–Enabled Channel Estimation Method in Intelligent Wireless Communication Systems

Through modeling the characteristics of wireless transmission channels, channel estimation can improve signal detection and demodulation techniques, enhance the spectrum utilization, optimize communication performance, and enhance the quality, reliability, and efficiency of intelligent wireless communication systems. In this paper, we propose a deep convolutional autoencoder–based channel estimation method in intelligent wireless communication systems. At first, the channel time‐frequency response matrix between the transmitter and receiver can be represented as 2D images. Then they are fed into the convolutional autoencoder to learn key channel features. To reduce the structural complexity of the deep learning model and improve its inference efficiency, we adopt the method of removing redundant parameters to achieve model compression. Iterative training and pruning based on stochastic gradient descent (SGD) and weight importance evaluation are alternated to obtain a lightweight deep learning model for channel estimation. Finally, extensive simulation results have verified the effectiveness and superiority of the proposed method.

Wireless Channel Measurements and Characterization in Industrial IoT Scenarios

Wireless Channel Measurements and Characterization in Industrial IoT Scenarios

Channel Estimation and Pilot Design for Uplink Sparse Code Multiple Access System based on Complex-Valued Sparse Autoencoder

Channel estimation is one of the most important aspects of wireless communication. Especially in Sparse Code Multiple Access (SCMA) system, the accuracy of channel estimation has a significant impact on decoding performance. Various methods, so far, have been developed for channel estimation. Most of these methods regard channel estimation as a parameter estimation problem of linear models. However, these methods require lots of time-frequency resources to ensure high estimation accuracy. In massive connection scenarios, high pilot overhead makes the spectrum resource more scarce. Therefore, the drawback of conventional channel estimation methods limits the further improvement of system capacity in the Internet of Things(IoT) when time-frequency resources is restricted. To address this problem, in this paper, we propose an efficient channel estimation scheme and sparse pilot structure design method in SCMA system based on complex-valued sparse autoencoder which is effective to learn features of wireless channel. Complex-valued sparse autoencoder is a kind of neural network with complex-valued weights. It contains two parts: encoder and decoder. In our work, the encoder part is used to realize pilot design. Channel estimation is implemented by the decoder. Complex-valued weights obtained from training are used as baseband pilots. Compared with maximum likelihood channel estimation (MLE) of linear model, the proposed method can achieve higher channel estimation accuracy with more sparse pilot structure. The bit-error rates performance of the SCMA receiver in our work is very close to that of the perfect channel state information (CSI).

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.

WiFOG: Integrating deep learning and hybrid feature selection for accurate freezing of gait detection

This study investigates the feasibility of utilizing non-invasive WiFi sensing using the 4.8 GHz operating frequency band of the 5 G spectrum, which is suitable for Internet of Things applications. We propose WiFOG: a WiFi CSI system for detecting FOG in PD leveraging deep learning and wireless channel characteristics collected by wireless devices such as a radio frequency signal generator, a network interface card, and dipole antennas. The raw data for several activities, including sitting, slow-walking, fast-walking, voluntary stopping, and FOG episodes, is collected. Regress feature engineering is performed in which discrete wavelet transforms is used for signal denoising and Hilbert-Huang transforms for feature extraction. Further, we propose hybrid feature selection techniques based on whale optimization, recursive feature elimination, and select form models for dimensionality reduction. Moreover, we propose a deep-gated recurrent network (DGRU) for activity classification and FOG detection and compared the results with the state-of-the-art approaches in the existing work. The results show our proposed scheme surpasses existing FOG detection with a total improvement of approximately 4% in accuracy and a 29% reduction in training time.

Design and Performance Analysis of Massive MIMO Modeling with Reflected Intelligent Surface to Enhance the Capacity of 6G Networks

Reflected Intelligent Surface (RIS) can establish a virtual line-of-sight link to enhance system performance while consuming less power in non-line-of-sight. RIS technique is utilized to minimize the spreading of electromagnetic signals by modifying the surface's electric and magnetic field characteristics. RIS can be adopted with the existing environment to effectively increase efficiency by modifying the radio channel characteristics. The signals are steered to the receiver using an RIS thus improving the link quality. The radio channel is viewed in traditional wireless systems as an uncontrollable entity that typically tampers with transmitted signals. This paper suggests an alternate theoretical model after examining the prevalent models and potential issues. Security of the wireless communication physical layer can be improved with an RIS by just changing the phase of the reflective unit. In this work, the massive Multiple Input Multiple Outputs (MIMO)-based iterative modeling technique is used to build the transmitter, channel, and receiver section. This will simultaneously optimize the RIS passive beamforming pre-coding matrix and thus avoid multi-user interference. At the base station cyclic programming is adopted, it iteratively calculates the active and passive beam-forming RIS matrices. The simulation results demonstrate that the combined pre-coding technique used in this work improves the weighted sum rate, efficiency, and coverage ratio. The results show that MIMO with continuous phase shift RIS achieves a higher Weighted Sum Ratio (WSR) and minimum Channel State Information (CSI) error in comparison with the random phase shift RIS. The performance metrics sum rate, signal-to-interference plus noise ratio (SINR), energy efficiency, and transmit power are analyzed and compared.