Wireless Channel Characteristics Research Articles

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.

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).

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.

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.

Ensuring Privacy in Wireless Channels with Physical Layer Security: A Survey of Encryption Strategies

This paper reviews various encryption strategies designed to enhance privacy in wireless communication, with a focus on leveraging physical layer security. Exploring techniques that utilize inherent wireless channel features for secure communication, the survey presents the strengths and challenges of each method. The aim is to provide a straightforward overview for researchers, practitioners, and decision-makers, about the evolving landscape of encryption methods in wireless channels for improved privacy. Furthermore, it discusses how these physical layer key generation techniques can be integrated with advanced technologies by providing a suggested model by combining the Machine learning and the Multi authority ciphertext-policy attribute-based encryption (CP-ABE) to ensure privacy in wireless channels.

A Novel Secure Routing Design Based on Physical Layer Security in Millimeter-Wave VANET

With the continuous development of millimeter-wave communication technology, new requirements such as ultra-reliability and higher data rates pose new challenges to the security issues of traditional cryptographic encryption in vehicular ad hoc networks (VANET). Physical layer security uses the characteristics of different wireless channels to protect the information security. In this paper, we propose a novel VANET routing mechanism that utilizes physical layer security to improve the secrecy performance, which is compatible with the millimeter-wave vehicular network. Specifically, we design a new secure routing selection factor, the utility function, that takes into account the effects of both secrecy rate and single-hop transmission distance to achieve the hop selection. In addition, we propose a novel routing mechanism and design a waiting mechanism based on the utility function. Compared with the traditional routing algorithms, the greedy perimeter stateless routing (GPSR) and Dijkstra simulation results illustrate that our design achieves superior performance in secrecy performance and dynamic adaptability.

Adaptive Physical-Layer Authentication for IoT in MIMO Communication Systems Using Support Vector Machine

Internet of things (IoT) devices have become ubiquitous due to the prevalence of the internet. However, the open nature of wireless networks makes them susceptible to spoofing attacks. Moreover, their heterogeneous characteristics create authentication challenges which are increasing due to the tremendous growth in the number and variety of devices. Physical layer authentication (PLA) provides a solution by utilizing the unique characteristics of wireless channels to aid upper layer authentication (ULA). In this paper, an adaptive physical layer authentication scheme is proposed which exploits the antenna diversity inherent in multi-input-multi-output (MIMO) systems. This scheme employs a one-class classification support vector machine (OCC-SVM) with the magnitude and real and imaginary parts of the received signals as features. Results are presented which show that this scheme provides robust authentication. The authentication performance is evaluated considering two majority voting schemes for IoT applications.

Channel Prediction With Time-Varying Doppler Spectrum in High-Mobility Scenarios: A Polynomial Fourier Transform Based Approach and Field Measurements

Beamforming for multi-antenna wireless communication systems has been widely studied and applied in practice. However, its performance in high mobility scenarios deteriorates dramatically due to severe channel aging. This issue stimulates the research of channel prediction in high mobility scenarios. This paper studies the general high-mobility Doppler domain wireless channel characterization and reveals that: 1) the Doppler frequency of the cluster corresponding to the near scatterers varies approximately linearly over short periods; 2) the linear-fitting characteristics of autoregressive model leads to poor channel prediction performance; 3) the parameter estimation algorithms of the chirp model which encompasses the characteristics are particularly complex. Given the shortcomings of existing works, this paper adopts the short-time Fourier transform to pre-estimate the parameter range and leverages the polynomial Fourier transform to obtain the initial values of the parameters. The downhill simplex algorithm is used to search for the fine-grained values of the parameters. In addition, the orthogonal matching pursuit algorithm is utilized to reconstruct the sparse signal. Evaluation results under the COST2100 channel model and a channel measurement campaign indicate that the proposed scheme can enhance the channel prediction accuracy or reduce the computing overhead compared to the existing matrix completion and polynomial iteration method.

Joint Heterogeneous PUF-Based Security-Enhanced IoT Authentication

This study proposes a novel authentication scheme that provides enhanced security for Internet of Things (IoT) applications by integrating a radio frequency (RF) physical unclonable function (PUF) with a device PUF. While traditional PUF-based authentication methods are lightweight, they are vulnerable to replay attacks because challenge-response pairs (CRPs) are exposed to adversaries over wireless channels. To prevent CRP exposure, we propose a joint scheme that integrates the physical layer features of wireless channels with those of the device PUF. Our authentication process consists of two stages: an enrollment stage and an authentication stage. During the enrollment stage, the physical features of the static random-access memory (SRAM) in the IoT devices are shared with the server, which generates a hashing model using the amplitudes of channel state information (CSI) as the RF-PUF and transfers it to the IoT device. In the authentication stage, the server and IoT devices exchange pilot signals to estimate the shared CSI. Both parties then generate the challenge information using the hashing model, which depends on the CSI. The challenge information is mapped to the response of the device PUF as the cryptographic key. If both parties are legitimate, the proposed model is updated through CSI amplitudes. We evaluated the proposed authentication protocol using a testbed based on Raspberry Pi and acquired a CSI dataset and SRAM-PUF using universal software radio peripheral and Arduino, respectively. Numerical results demonstrate that our method effectively defends against diverse attacks, even in critical CRP exposure scenarios.

Super-Resolution of Wireless Channel Characteristics: A Multitask Learning Model

Super-Resolution of Wireless Channel Characteristics: A Multitask Learning Model