Propagation Channel Characteristics Research Articles

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

Characterizing in-band full-duplex in broadcast communications: From field trials to a loopback channel model

In-band full duplex (IBFD) communications are a potential solution to spectrum scarcity. IBFD communications offer greater spectral efficiency than traditional half-duplex communications by transmitting and receiving on the same frequency channel. However, IBFD operation requires overcoming the challenge of eliminating the self-interference coupled from the transmit antenna to the receive subsystem. Knowledge of the characteristics of the loopback propagation channel makes it easier to cancel out the self-interference. Nevertheless, complex field trials are required to adequately characterize loopback channels, which is still lacking in the recent literature. This paper proposes a measurement campaign in a real and ongoing broadcast transmission center to characterize the main characteristics of loopback channels. The work proposes a set of loopback channels based on field trials and an empirical analysis of the most relevant parameters of the channel model, such as the Doppler spectrum, the delay spread, and the K-factor.

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.

6G THz Propagation Channel Characteristics and Modeling: Recent Developments and Future Challenges

The terahertz (THz) band is expected to play a more important role in the sixth generation (6G) communication networks. It can provide large bandwidth and high-speed transmission rate to support prospective 6G scenarios from space to ground and from macro-scale to nano-scale. However, the THz channel characteristics vary greatly for different scenarios. This paper presents a multi-dimensional overview of propagation channel characteristics and modeling for potential THz communication scenarios, including ground, space, and nano-scale communications. The atmospheric attenuation and propagation mechanisms are firstly discussed for ground scenarios. Then, the channel measurements, characteristics, and models for specific ground scenarios, including indoor office, data center, kiosk, smart rail, and massive MIMO are investigated. Following this, the THz space communication channels are summarized according to the satellite altitude. Futhermore, THz nano-scale communication channels including on-chip and body centric are analyzed. Finally, future research challenges are outlined.

Deep Learning Based Cooperative MIMO Systems for Wireless Body Area Networks

Wireless Body Area Network (WBAN) is widely applied in various fields, including healthcare, sports, wellness, and assistive technologies, by offering the benefits of convenience, reliability, low latency, privacy, and customization. However, the propagation characteristics of the WBAN channel can impact the reliability of transmission, which is particularly crucial in healthcare systems. To address this issue, this article presents a novel approach using deep learning-based cooperative Multiple-Input Multiple-Output (MIMO) systems that leverage the autoencoder (AE) technique. In our proposed approach, we utilize the AE-based cooperative MIMO systems with two different techniques: Amplify-and-Forward (AE-AF) and Decode-and-Forward (AE-DF). The AE-AF scheme operates without needing training parameters at the relay node, whereas the AE-DF scheme necessitates training parameters at the relay node. Both schemes aim to overcome challenges such as multipath propagation phenomena, thereby enhancing the performance of on-body communication systems. Additionally, we introduce two combinators, Minimum Mean Square Error (MMSE) scheme and Radio Transformation Network (RTN), to effectively mitigate co-channel interference (CCI) in the received signal streams and improve the bit error rate performance of the AE-AF and AE-DF systems. We assess the performance of these systems in scenarios with and without direct links. Simulation results demonstrate significant performance improvements compared to baseline cooperative MIMO systems using MMSE combining, namely AF-MMSE and DF-MMSE systems. Notably, the proposed systems employing RTN combination, including both direct and relay paths, achieve a 7.5 dB gain over the baseline when all nodes are equipped with two transceiver antennas.

A Comparative Evaluation of Propagation Characteristics of Vehicular VLC and MMW Channels

A Comparative Evaluation of Propagation Characteristics of Vehicular VLC and MMW Channels

New Mid-Band for 6G: Several Considerations from the Channel Propagation Characteristics Perspective

New Mid-Band for 6G: Several Considerations from the Channel Propagation Characteristics Perspective

Analysis of Propagation Characteristics in Unmanned Aerial Vehicle (UAV) System

Unmanned Aerial Vehicle (UAV) has gained great attention to the spread of communication in civilian and military applications. UAV communication channel has its own characteristics compared to cellular and satellite systems, which are widely used. Thus, an accurate channel characterization is very important to optimize the performance and design of an efficient UAV communication system. However, several challenges exist in UAV channel modeling. For example, channel propagation characteristics of UAVs are still less explored. Therefore, this research discusses the propagation characteristics of UAV communication systems. Due to the limitation of the measurement tools, the propagation characteristics identified in this research was the pathloss coefficient value and optimum height based on the value of Received Signal Strength Indicator (RSSI) measurement results at different distance and heights. The link communication used 433 MHz telemetry. The results of pathloss coefficient at heights of 10 m, 20 m, and 30 m are 1.56 m, 1.77 m, and 1.99 m. While the results of the optimum height of 10 m, 20 m, and 30 m are 1.39 m, 1.32 m, and 1.47 m.

Measurement-based fading characteristics analysis and modeling of UAV to vehicles channel

With the rapid development of unmanned aerial vehicle (UAV) and autonomous driving technology, wireless communication between UAV and vehicles has become one of the hotspots in the research of intelligent transportation systems (ITS). Particularly, link-level UAV-based communication requires correlation characteristics of propagation channel. In the current channel measurement, the transmitter or receiver of the ground is fixed, which ignores the high dynamics of the vehicle and the complexity of the environment in the ITS scene. Therefore, it is necessary to conduct a dynamic channel measurement and analysis for UAV. In this paper, we carry out an UAV-to-Vehicle (U2V) measurement campaign in S- and C-band for multiple scenarios of low-altitude UAV and mobile vehicles propagation and provide a comprehensive investigation of channel fading characteristics. Based on the measurement data, the statistics of large-scale fading (path loss, shadow fading and its autocorrelation) and small-scale fading (amplitude distribution) for several typical measurement scenarios are extracted first, which are compared with other air-to-ground (A2G) and standard terrestrial propagation scenarios to analyze the U2V propagation characteristics in various scenarios. A comprehensive analysis and comparative study of all considered channel parameters extracted is then performed to reflect the physical laws behind the measurements. The analysis results reveal that the Log-distance model outperforms the considered typical models in terms of predicting the path loss, and the proposed autocorrelation model shows better performance than traditional models. The quantitative results are essential for modeling and realizing reliable communications in U2V wireless systems and analyzing the performance for UAV-enabled ITS.

Propagation channel characterization for mobile communication based on measurement campaign and simulation

Predicting the coverage area of mobile networks across various frequency bands is a critical concern in wireless communication. Each frequency band exhibits distinct propagation characteristics that directly influence the network coverage. Therefore, the use of the appropriate propagation models, whether through simulation or measurement-based approaches, is essential for accurate signal strength prediction in different environments and scenarios. In this study, we focus on the analysis and comparison of propagation channel characterization for mobile communication within the sub-6GHz frequency range. Our research involved outdoor measurement campaigns and ray-tracing simulations conducted at the Botswana International University of Science and Technology, covering distances up to 213 m. We then conducted an in-depth investigation and comparative analysis of the measured and simulated data for the propagation channel. Additionally, we derived a characterization model for the closed-in free space reference distance (CI) model based on measured and simulated data. Finally, we employed the root-mean-square error (RMSE) as a quantitative assessment of the performance of the characterized models. Notably, our findings revealed that the derived Close-in model based on measured data outperformed that of the simulations, exhibiting the lowest RMSE values of 3.56 dB, 4.20 dB, and 7.87 dB for location-1 line-of-sight, location-2 line-of-sight, and non-line-of-sight scenarios, respectively. These results hold significant potential for the development of precise path loss models that can effectively predict and optimize the coverage area of mobile communication systems across various real-world scenarios and environments.

Quantitative Experimental Evaluation of RFID Propagation Loss with Wooden and Metal Bookshelves

Radio frequency identification (RFID) is wireless multimedia applications for bookshelves experimental system to replace the barcodes media technology. The RFID propagation channel characteristics and environment effect should be known. In this study to evaluate the RFID propagation loss with wooden and metal bookshelves based on data of measurement. Experimental study evaluation of RFID multimedia system with bookshelves is using vector network analyzer (VNA) and the microstrip patch antennas of transmitter (Tx) and receiver (Rx) antennas at a frequency range from 2.4 GHz to 2.5 GHz. The results of experiment are considering the path loss, received signal strength (RSS), and comparison the path loss differences with cumulative distribution function (CDF) to evaluated, respectively. In this research work are necessary for RFID antenna design and evaluate the RFID multimedia systems.

Free-space optical (FSO) feeder link planning in space-ground integrated optical networks (SGIONs): trade off throughput and dynamics.

The low-orbit satellite communication can provide users with low-delay and ultra-wideband communication services worldwide. By the wide available bandwidth and immunity to interference, free-space optical (FSO) feeder links are presented as an ideal alternative to radio frequency (RF) feeder links for satellite-to-ground backhaul, and the space-ground integrated optical network (SGION) is gradually formed by using FSO feeder links to integrate the low-orbit satellites and the terrestrial optical network (TON) for backhaul services. The propagation channel characteristics of FSO feeder links vary significantly during low-orbit satellite passes, and atmospheric turbulence causes serious scintillation in FSO feeder links at low elevations, increasing link budget and restricting link capacity. Limiting the observation range of optical ground stations (OGSs) to the high elevation area can establish high-capacity feeder links to improve SGION's throughput, but inevitably increase the network dynamics and reduce the satellite visibility. This paper trades off SGION's throughput and dynamics by planning FSO feeder link handover and capacity adjustment. Two baseline schemes and the feeder link handover and capacity adjustment scheme based on non-dominated sorting genetic algorithm (NSGA-FLHCA) are proposed. By finding the Pareto edge of the multi-objective optimization problem, NSGA-FLPCA is more effective in improving network throughput and reducing network dynamics compared with two baselines.

Scenario-specific radio propagation simulation from LiDAR point cloud data for smart office scenario

Recently, the cyber-physical-system (CPS) has attracted great interest in the information and communications technology (ICT) research field for the virtual test of wireless communication systems. Since the performance of wireless communication significantly depends on the radio propagation channel characteristics, the development of accurate radio propagation simulation technology becomes essential for the purpose. This paper proposed the scenario-specific radio propagation simulation method from LiDAR point cloud data. By the continuous environment measurement by LiDAR, the positions of dynamic objects like pedestrians are tracked, as well as the site-specific propagation environment characteristics and their influences are taken into the simulation. For performance evaluation, the radio propagation measurement was conducted in the 28 GHz band in an indoor office. The result showed a significant improvement in the RMSE of received power prediction. The proposed method will be utilized for the radio channel emulation in the CPS.

Adversarial training-aided time-varying channel prediction for TDD/FDD systems

In this paper, a time-varying channel prediction method based on conditional generative adversarial network (CPcGAN) is proposed for time division duplexing/frequency division duplexing (TDD/FDD) systems. CPcGAN utilizes a discriminator to calculate the divergence between the predicted downlink channel state information (CSI) and the real sample distributions under a conditional constraint that is previous uplink CSI. The generator of CPcGAN learns the function relationship between the conditional constraint and the predicted downlink CSI and reduces the divergence between predicted CSI and real CSI. The capability of CPcGAN fitting data distribution can capture the time-varying and multipath characteristics of the channel well. Considering the propagation characteristics of real channel, we further develop a channel prediction error indicator to determine whether the generator reaches the best state. Simulations show that the CPcGAN can obtain higher prediction accuracy and lower system bit error rate than the existing methods under the same user speeds.

Nakagami-m Fading Channel Identification Using Adaptive Continuous Wavelet Transform and Convolutional Neural Networks

Channel identification is a useful function to support wireless telecommunication operations because the knowledge of the radio frequency propagation channel characteristics can improve communication efficiency and robustness. In recent times, the application of machine learning (ML) algorithms to the problem of channel identification has been proposed in the literature. In particular, Deep Learning (DL) has demonstrated superior performance to ’shallow’ machine learning algorithms for many wireless communication functions. Inspired by the success of DL in literature, the authors in this paper apply Convolutional Neural Networks (CNN) to the problem of channel identification, which is still an emerging research area. CNN is a deep learning algorithm that has demonstrated superior performance to ML algorithms, in particular for image processing tasks. Because the digitized RF signal is a one-dimensional time series, different algorithms are applied to convert the time series to images using various Time Frequency Transform (TFT) including the CWTs, spectrogram, and Wigner Ville distribution. The images are then provided as input to the CNN. The approach is applied to a data set based on weather radar pulse signals generated in the laboratory of the author’s facilities on which different fading models are applied. These models are inspired by the tap-delay-line 3GPP configurations defined in the standards, but they have been customized with Nakagami-m fading distribution (3GPP-like fading models). The results show the superior performance of time–frequency CNN in comparison to 1D CNN for different values of Signal to Noise Ratio (SNR) in dB. In particular, the study shows that the Continuous Wavelet Transform (CWT) has the optimal performance in this data set, but the choice of the mother wavelet remains a problem to be solved (this is a well-known problem in the research literature). Then, this study also proposes an adaptive technique for the choice of the optimal mother wavelet, which is evaluated on the mentioned data set. The results show that the adaptive proposed approach is able to obtain the optimal performance for most of the SNR conditions.

A 5.9 GHz Channel Characterization at Railroad Crossings for Train-to-Infrastructure (T2I) Communications

Intelligent transport systems (ITSs) rely on wireless communications that provide many services to ground and aerial vehicles. We believe that vehicular communication protocols can evolve the train communication systems into the next generation. However, we found that channel models in train track environments at the 5.9 GHz frequency band are scarcer than in vehicular environments. Therefore, we conduct channel measurements at the 5.86–5.91 GHz ITS band at various railroad crossings in the United States. This allows us to extract the channel parameters and evaluate the propagation channel characteristics. The evaluations show a certain similarity between the train track channel characteristics and the vehicular communications channel characteristics. The railroad channel with an omnidirectional antenna is similar to a suburban environment in the vehicular channel, and with a bidirectional antenna, it is similar to a highway LoS environment in the vehicular channel. However, more importantly, the population of the surrounding buildings and the size of the LoS window can highly affect the RF propagation characteristics.

A Probabilistic Propagation Graph Modeling Method for Channel Characterization in Dense Urban Environments

With the rapid development of mobile broadband communications such as the “fifth generation (5G)” systems, accurate and efficient simulation-based channel modeling becomes more essential for propagation channel characterization in the complicated environments. In the previous works, the so-called “propagation-graph-based channel modeling” method has been introduced and effectively applied to the simulation of channel characteristics. Compared with the conventional ray-tracing-based approaches, the graph-based modeling may significantly reduce the computational complexity. In this contribution, without increasing computational complexity, the graph-based channel modeling method is proposed for channel characterization in the dense urban environments with the following improvements, i.e. realistic geometry-assisted scattering point placement, scattering coefficient definition, and line-of-sight (LoS) path presence probability settings. By comparing the simulated channel characteristics with those obtained in the real measurements in two urban scenarios, the proposed improvements are demonstrated in predicting channel characteristics more accurately.

Impact of Antenna Design, Electromagnetic Propagation, and RF Frequency on the Performance of Wireless Communication Systems in Urban Environments

The design and optimization of antennas, electromagnetic propagation, radio frequency (RF), and channel characterization are vital to the performance of the system in the rapidly developing area of wireless communication. Antennas are electronic components that transform electrical currents into electromagnetic waves and vice versa. They are critical to the operation of any wireless network and their design and optimization are therefore of paramount importance. The goal of this research was to find out how different factors, like antenna type, electromagnetic environment, RF, and channel characteristics, affect the efficiency of a wireless network. Multipath fading of 8 dB, shadow fading of 3 dB, and path loss of 100 dB were used in the study with a microstrip antenna in a suburban propagation scenario at an RF frequency of 5 GHz. Antenna design, electromagnetic propagation scenario, radio frequency (RF), and channel characteristics were all shown to have an impact on the wireless communication system performance. The performance metrics of the wireless sensor network were almost, but not quite, those that were hoped for in a real-world setting. Several avenues for future study are suggested, and the results have important implications for the design and optimization of wireless communication systems.

Propagation Measurement and Channel Characteristics of Small Office OAM Communication at 30 GHz

Orbital angular momentum (OAM) communication has been envisioned as a potential technology to increase capacity for 6G communication. In this letter, we present an OAM channel measurement campaign in an indoor small office at 30 GHz. Furthermore, the path loss is investigated and modeled by close-in (CI) model and floating-intercept (FI) model. It is found that the FI model fits the measured path loss better than the CI model, and the CI model may no longer appropriate for OAM wave propagation when the receiver is in the hollow radiation area of the OAM beam. Also, we investigate the K-factor and the results indicate that the divergence angle leads to more complicated multipath propagation characteristics of the wireless channel. Moreover, root mean square delay spread reveals that channel performance can be effectively improved when the receiver is within the direct radiation range of the OAM antenna. Generally, this work can be considered as a foundation of understanding and building channel models of indoor OAM communication systems.

AI-Based Time-, Frequency-, and Space-Domain Channel Extrapolation for 6G: Opportunities and Challenges

The trend of using larger scale antenna arrays will continue toward 6G systems, where the number of antennas will be further scaled up to improve spectral efficiency. However, the increase in the number of antennas will bring new challenges to the physical layer, such as frequent feedback in high-speed mobile communications, multiband coexistence overhead from sub-6 (gigahertz) GHz to terahertz (THz), and energy consumption due to increased antenna components and circuits. In this article, we introduce artificial intelligence (AI)-based channel extrapolation to address these problems. Specifically, we divide the channel extrapolation into time, frequency, and space domains according to different application scenarios. The channel propagation characteristics that affect the extrapolation of each domain, such as the spatial consistency property (SCP), partial reciprocity, and spatial nonstationarity, are analyzed. The motivations for selecting various AI models in each domain are explained, and the performance of AI models is compared. Furthermore, we find the gain of cross-domain channel extrapolation based on transfer learning (TL). The simulation results show that the experience of the AI model cross different domains can be mutually reinforcing. Finally, we introduce several challenges for AI-based channel extrapolation, which can be regarded as potential research directions for realizing future AI-powered 6G systems.