Channel Model Research Articles

Performance evaluation of vehicular communication in terahertz band

Abstract The Terahertz (THz) band is seen as one of the potentially essential technologies to meet the data throughput requirements of terabits per second (Tbits) in future networks. Considering vehicular communication in particular, the current vehicular communication technologies cannot cope with the rapidly increasing data traffic. To deliver increased capacity and data rate with reduced latency, new technologies and methodologies are therefore required. It is recognized that one possible technology to meet these needs is THz technology. Since the change in distance between vehicles due to their speed affects THz communication, channel modeling was carried out for single-lane and multi-lane vehicular communication scenarios in this study. The system’s performance was also examined. Performance metrics were determined as path loss, signal-to-noise ratio (SNR), capacity and bit error rate (BER). While performing these analyses, the absorption of THz waves is taken into account and the High-Resolution Transmission Molecular Absorption Database (HITRAN) was used to model it. As a result, the most appropriate circumstances for successful vehicular communication have been identified and studied.

Circumpolar Transport and Overturning Strength Inferred From Satellite Observables Using Deep Learning in an Eddying Southern Ocean Channel Model

AbstractThe Southern Ocean connects the ocean's major basins via the Antarctic Circumpolar Current (ACC), and closes the global meridional overturning circulation (MOC). Observing these transports is challenging because three‐dimensional mesoscale‐resolving measurements of currents, temperature, and salinity are required to calculate transport in density coordinates. Previous studies have proposed to circumvent these limitations by inferring subsurface transports from satellite measurements using data‐driven methods. However, it is unclear whether these approaches can identify the signatures of subsurface transport in the Southern Ocean, which exhibits an energetic mesoscale eddy field superposed on a highly heterogeneous mean stratification and circulation. This study employs Deep Learning techniques to link the transports of the ACC and the upper and lower branches of the MOC to sea surface height (SSH) and ocean bottom pressure (OBP), using an idealized channel model of the Southern Ocean as a test bed. A key result is that a convolutional neural network produces skillful predictions of the ACC transport and MOC strength (skill score of 0.74 and 0.44, respectively). The skill of these predictions is similar across timescales ranging from daily to decadal but decreases substantially if SSH or OBP is omitted as a predictor. Using a fully connected or linear neural network yields similarly accurate predictions of the ACC transport but substantially less skillful predictions of the MOC strength. Our results suggest that Deep Learning offers a route to linking the Southern Ocean's zonal transport and overturning circulation to remote measurements, even in the presence of pronounced mesoscale variability.

A Novel 3D Non-Stationary Vehicle-to-Vehicle Channel Model with Circular Arc Motions

A Novel 3D Non-Stationary Vehicle-to-Vehicle Channel Model with Circular Arc Motions

Multi-grooved channel design in continuous casting mold for enhancing heat transfer efficiency considering pressure drop and flow rate loss

An efficient, speedy, multi-grooved (ESMG) mold was designed and manufactured for optimization to address issues such as low heat transfer rate, slow casting speed, and quality defects in traditional continuous casting molds. The flow resistance mechanism of multi-grooved channels with varying parameters was investigated by considering the ESMG geometric model, the convective heat transfer characteristic variation trends were revealed with different channel designs. Considering constraints of the dimensional chain and supply pressure, variation trends and mechanisms of the pressure drop, flow rate loss, and convective heat transfer coefficient of the ESMG mold were explored using multiple channel variables. Based on the numerical model of the ESMG channel, temperature variation trends in the copper mold were verified by comparison with relevant literature data, supporting the convective-heat-transfer model and variation trends of the ESMG mold. A high-efficiency heat-transfer ESMG assembly that casts U71Mn high-carbon large rectangular billets was fabricated, achieving a closed-loop dimensional chain and replacing traditional molds. Experimental validation on the continuous casting machine (CCM) proved directly that redesigning the ESMG mold cooling channel improved heat transfer efficiency and reduced CO2 emissions. After 504 h on the CCM, the ESMG mold casting speed increased from 1.1 to 1.6 m/min, the heat transfer efficiency was 17.6% higher than that of traditional molds and CO2 emissions were estimated to decrease by 31.2%. The billets produced by the ESMG mold had no quality defects in shape or surface with the original casting conditions, which provided enhanced support for accelerating continuous casting lines.

An empirical study of ISAC channel characteristics with human target impact at 105 GHz

AbstractLeveraging the ultra‐wideband advantages of the terahertz band, Integrated sensing and communication (ISAC) facilitates high‐precision sensing demands in human smart home applications. ISAC channel characteristics are the basis for ISAC system design. Currently, the ISAC channel is divided into target and background channels. Existing researches primarily focus on the attributes of human target itself, e.g. radar cross‐section and micro‐Doppler effect. However, the impact of human target on neither the pathloss characteristic of background channel nor the multipath propagation characteristic of target channel is considered. To address the gap, we conduct indoor channel measurements at 105 GHz to investigate the ISAC channel characteristics with the impact of human target. Firstly, by analysing the power angular delay profiles with and without human target, the changes in quantity and power of multipath components (MPCs) are observed. Then, a parameter called power control factor is proposed to evaluate the human target impact on pathloss, thereby modifying the existing pathloss model of background channel. Eventually, the MPCs belonging to target channel are extracted within target‐oriented power delay profile to count the power proportion of each bounce MPCs of the target‐Rx link, which supports the necessity of multi‐bounce (indirect) paths modelling in target channel.

Computer modeling of polypeptide toxins and ion channels: Achieving success through failure

Computer modeling of polypeptide toxins and ion channels: Achieving success through failure

BER and outage throughput analysis of DPIM/DHPIM coded QPSK-OFDM based outdoor optical wireless communications

With the growing demand for fast and reliable communication systems, particularly in outdoor environments, it is essential to investigate advanced encoding techniques. Digital Pulse Interval Modulation (DPIM) and Dual Header Pulse Interval Modulation (DHPIM) emerge as promising alternatives to traditional line coding methods, providing enhanced spectral efficiency and resistance to signal disruptions. This paper presents the performance of a Quadrature Phase-Shift Keying (QPSK) Orthogonal Frequency Division Multiplexing (OFDM)-based Optical Wireless Communication (OWC) system using these advanced encoding schemes. The analysis includes simulation results on QPSK-OFDM-based transmitter design, free space optical channel modeling based on Gaussian and log-normal distribution atmospheric turbulence, and recovery of input digital stream using the mentioned line coding techniques. The results demonstrate optimal bit error rate (BER) values for the QPSK-OFDM-based OWC system. The primary innovation of this research lies in the encoding schemes and their performance in outdoor optical wireless communication systems under turbulent conditions. Through extensive simulations and analysis, detailed insights into the bit error rate and outage throughput characteristics of DPIM/DHPIM coded QPSK-OFDM are provided, offering a unique perspective on the performance of these schemes in real-world scenarios. The findings not only highlight the optimal BER values achievable with the QPSK-OFDM-based OWC system but also offer insights into the system's ability to overcome transmission challenges under various atmospheric conditions.

Graph Neural Network Enabled Propagation Graph Method for Channel Modeling

Graph Neural Network Enabled Propagation Graph Method for Channel Modeling

On the effect of wave amplitude and corrugation profile of certain types of corrugated channels on thermal performance and entropy generation

This study aims to examine experimentally and numerically the thermal-hydraulic performance and entropy generation of wavy channels: sinusoidal and triangular, and compare them with respect to the straight channel. Various wavy amplitudes and corrugation profiles are considered in a sinusoidal wavy channel and a triangular wavy channel and assessed with the straight channel. Experimental test rig is built and the experiments are carried out on laminar air flow and used to verify the simulation results. The main findings show that the HT rate, fluid flow, and entropy generation are affected by the corrugation profile and wave amplitude of the wavy channel. The maximum Nusselt number of 11.17 occurred in a sinusoidal wavy channel with a wave amplitude equal to 1.75, and high friction factor also happens in this model. It was found that the sinusoidal and triangular wavy channels with A = 0.75 and a flow rate range of 1–1.75 have the highest overall thermal performance factor. Also, the total entropy generation of the straight channel was lower than all corrugated channel models. In conclusion, it has been found that the best compromise between enhanced HT and the accompanying increase in both pressure drop and entropy generation is occurred in case of A = 0.75 at flow rate ranged from 1 to 1.67 LPM.

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.

Sample selection bias in non-traditional lending: A copula-based approach for imbalanced data

Credit scoring models for non-traditional lending channels, such as peer-to-peer (P2P) lending platforms, are usually estimated only on the sample of accepted applicants. This may lead to biased estimates of the risk drivers. This issue can be addressed using a reject inference technique that includes the characteristics of rejected applicants in the model. Due to the low numbers of accepted applicants and default records, credit scoring models usually face a class imbalance problem. However, previous literature on sample selection models for credit scoring does not address the class imbalance issue. To fill this gap, we extend the Generalised Extreme Value (GEV) regression model for binary data to the sample selection framework. We consider the quantile function of the GEV distribution as a link function in both the selection and outcome equations. We use the copula function to model the dependence structure between the two equations for its flexibility. This proposal is called the Sample Selection Generalised Extreme Value (SSGEV) model and it is implemented in the R package BivGEV. We apply this model to a comprehensive dataset provided by Lending Club, and we show that parameter estimates obtained only on accepted P2P applicants are biased and coherently with the literature. The SSGEV model achieves a higher predictive accuracy than those obtained using univariate approaches or a sample selection probit model. Our proposal also provides more conservative estimates of the Value-at-Risk and the Expected Shortfall.

The Impact of Flow Patterns Due to the Cold Lahar Flooding of Mount Semeru on the Collapse of the Bridge Connecting Malang District and Lumajang District

The flow patterns that occur in rivers as a result of falling cold lava often cause damage here and there. This incident cannot be separated from the large volume of water mixed with material that crosses the open channel which is caused by input water supply or water supply from river reservoirs crossed by cold lava. by water researchers for water mixed with volcanic eruption material suspended above. The event of water flowing through a river is influenced by the presence of the surface profile of the river bed and the various widths of the river that occur. If a volume with a certain viscosity is given, a thick flow will be formed that can be seen on the surface of the river, while that which is not visible to the naked eye above the water surface is called thick viscosity. If the runoff of cold lava material that occurs in the channel is not measured, it will result in wild jumps. The wild and viscous jumping structure that has been arranged can be observed as to how the flow pattern occurs. To avoid not detecting the flow pattern, water measuring structures are needed, one of which is a threshold with openings below or what is known as flow above the threshold along with existing bridge buildings around it. This sluice gate is easy to operate manually. The need for water operations with excess water availability is not an easy thing to calculate, because of the influence of depth and jumps that occur around the bridge, hydraulic jump patterns often ignore this measurability, this will be an obstacle if there is a large supply of cold lava. mixed with rainwater during peak conditions in the drainage process. Observations of this completion need to be proven by carrying out model tests using a tool known as the Threshold which produces a water jump along with the material. This requires further investigation for research in the model channel. For modeling thresholds and bridges connecting between plains, this tool provides instruments and many threshold treatments as well as regulating pressurized water with a pump at a certain flow rate in a measurable manner. To analyze the height of building damage, determine the flow pattern, speed by observation, test results with valve openings on certain pumps, researchers tested and measured the position of cold lava deposits one by one, the position of the slope of the river in certain sections, starting with an opening of 0.5; 0.75, read the instrument, move the threshold position from a bridge distance of 20 cm, 40 cm, to the furthest distance of 240 cm. Make graphs of test results for the threshold of the bridge opening transfer tool by testing the tool and opening 3 times for each pump opening, recording, mapping the depth values, jump length and travel time of the instrument on the test tool. Recommend the results of the analysis of the Threshold and Bridge tests to provide information to bridge construction planners that the test results with various threshold positions in certain conditions have the value of the Glidik II bridge collapse, the jump with the travel time of the speed of passing cold lava. Next, the bridge is determined and the next threshold can be used to determine how severe the damage is to the river. This research is laboratory research with threshold and bridge models. The target of this output is a journal article resulting from a simulation of determining the flow pattern that occurs in the channel by shifting the position of the threshold relative to the location of the bridge from the channel model with various diameter sizes of the model material, the results of which are in the form of a table and graphed on the part modeled in the water laboratory, especially flow with sediment and cold lava material. The results of this section were observed up to the collapse of the bridge at a depth of 4.5 cm with an opening of ½ second to 37 (on the model scale) observed or a depth of 45 meters (on the empirical scale). to then be written in the form of an article to be included in an international journal.

Modeling and Performance Study of Vehicle-to-Infrastructure Visible Light Communication System for Mountain Roads.

Visible light communication (VLC) is considered to be a promising technology for realizing intelligent transportation systems (ITSs) and solving traffic safety problems. Due to the complex and changing environment and the influence of weather and other aspects, there are many problems in channel modeling and performance analysis of vehicular VLC. Unlike existing studies, this study proposes a practical vehicle-to-infrastructure (V2I) VLC propagation model for a typical mountain road. The model consists of both line-of-sight (LOS) and non-line-of-sight (NLOS) links. In the proposed model, the effects of vehicle mobility and weather conditions are considered. To analyze the impact of the considered propagation characteristics on the system, closed-form expressions for several performance metrics were derived, including average path loss, received power, channel capacity, and outage probability. Furthermore, to verify the accuracy of the derived theoretical expressions, simulation results were presented and analyzed in detail. The results indicate that, considering the LOS link and when the vehicle is 50 m away from the infrastructure, the difference in channel gain between moderate fog and dense fog versus clear weather conditions is 1.8 dB and 3 dB, respectively. In addition, the maximum difference in total received optical power between dense fog conditions and clear weather conditions can reach 76.2%. Moreover, under clear weather conditions, the channel capacity when vehicles are 40 m away from infrastructure is about 98.9% lower than when they are 10 m away. Additionally, the outage probability shows a high correlation with the threshold data transmission rate. Therefore, the considered propagation characteristics have a significant impact on the performance of V2I-VLC.

Semantic-empowered visible light communication for image transmission based on deep convolutional generative adversarial network

In this paper, a semantic communication-based scheme was proposed to tackle the optimization challenge of transmission efficiency and link stability in indoor visible light communication (VLC) systems utilizing light-emitting diodes for image transmission. The semantic model, established by deep convolutional generative adversarial network (DCGAN) and vector quantization method, can effectively extract the essential characteristics of images. In addition, indoor VLC channel models including line-of-sight (LOS) and non-line-of-sight (NLOS) links are established in a 5*5*3 room, while incorporating noise interference encountered during signal transmission into the training process of the semantic model to enhance its anti-interference capability. Besides, the performance disparity between the semantic VLC system and the conventional VLC system using Better Portable Graphics (BPG) is assessed across various modulation formats, transmission distances, and receiving locations. The simulation results demonstrate that the semantic VLC system effectively enhances link stability and achieves a signal-to-noise ratio (SNR) gain exceeding 6 dB. Additionally, the semantic VLC system achieves superior performance compared to the conventional VLC system employing convolutional code at a rate of 1/2, without incurring additional bits consumption for error correction. Moreover, the practicality of the semantic VLC system is experimentally validated on an indoor physical testbed online enabled by the field programmable gate array (FPGA) with up to 1 Gbps data rate. In comparison with existing schemes, the semantic VLC system effectively reduces communication overhead by 40% while maintaining similar received image quality. Furthermore, it significantly enlarges the communication coverage of the VLC system by a factor of two without necessitating any hardware modifications and ensures stable transmission of images in high-speed scenarios.

Sub-terahertz near field channel measurements and analysis with beamforming and Bessel beams

Sub-terahertz communications (100–300 GHz) are explored today as a candidate technology to enable extremely high-rate, low-latency data services and high-resolution sensing in beyond-fifth-generation (beyond-5G) wireless networks. However, these sub-terahertz wireless systems will often have to operate in the near field, where the signal propagation does not follow canonical far-field models, including the commonly used free space path loss equation. Instead, the signal propagation in the near field follows more complex patterns that are not well-captured with analytical far-field models standardized for 5G research. Moreover, state-of-the-art beamforming solutions exploited heavily in fourth-generation (4G) and 5G networks are notably less efficient in the near field. In this article, the near-field sub-terahertz channel is accurately measured and analyzed. In addition to state-of-the-art beamforming, the article also analyzes the sub-terahertz channel measurements when using near-field-specific Bessel beams that demonstrate fewer power fluctuations in the near field in addition to higher focusing gain. Novel distance-centric and angle-centric dependencies reported in this article may serve as a reference when developing next-generation channel models for sixth-generation (6G) and beyond-6G near-field sub-terahertz wireless systems.

Ground-to-UAV sub-terahertz channel measurement and modeling

Unmanned aerial vehicle (UAV) assisted wireless communications have been expected to play a vital role in the next generation of wireless networks. UAVs can serve as either repeaters or data collectors within the communication link, thereby potentially augmenting the efficacy of communication systems. Despite their promise, the channel analysis and modeling specific to terahertz (THz) wireless channels leveraging UAVs remain under-explored. This work delves into a ground-to-UAV channel at 140 GHz, with a specific focus on the influence of UAV hovering behavior on channel performance. Employing experimental measurements through an unmodulated channel setup and a geometry-based stochastic model (GBSM) that integrates three-dimensional positional coordinates and beamwidth, this work evaluates the impact of UAV dynamic movements and antenna orientation on channel performance. Our findings highlight the minimal impact of UAV orientation adjustments on channel performance and underscore the diminishing necessity for precise alignment between UAVs and ground stations as beamwidth increases.

A visible light positioning system for coal mine personnel based on convolutional recurrent neural network

Visible Light Positioning (VLP) technology exhibits promising potential for underground coal mine positioning. While numerous studies have explored VLP in indoor environments, attention to VLP systems for underground mines still needs to be improved. In this paper, based on the traditional indoor visible light communication channel model, the effects of unique features on VLP, such as irregular walls, inclined and rotating receiving ends, and coal dust particles in underground mines, are considered and modeled in detail. In order to improve the accuracy and reliability of personnel VLP in complex mine environments, this paper proposes a deep-learning-based VLP system for underground personnel in coal mines, which consists of two LEDs and four photodetectors (PDs), where the four PDs are mounted on miners' helmets, in conjunction with the actual mine environment. Due to environmental factors and noise, there are apparent spatio-temporal characteristics in the received signal strength of the PDs. Therefore, the system adopts a convolutional recurrent neural network (CRNN) for feature extraction. In the experimental phase, the method's average localization error was 11.24 cm, with 90% of the localization errors within 20.4 cm. The experimental results show that the system has high positioning accuracy and meets the requirements of personnel positioning in underground mines.

Inhibitors of the small membrane (M) protein viroporin prevent Zika virus infection.

Flaviviruses, including Zika virus (ZIKV), are a significant global health concern, yet no licensed antivirals exist to treat disease. The small Membrane (M) protein plays well-defined roles during viral egress and remains within virion membranes following release and maturation. However, it is unclear whether M plays a functional role in this setting. Here, we show that M forms oligomeric membrane-permeabilising channels in vitro, with increased activity at acidic pH and sensitivity to the prototypic channel-blocker, rimantadine. Accordingly, rimantadine blocked an early stage of ZIKV cell culture infection. Structure-based channel models, comprising hexameric arrangements of two trans-membrane domain protomers were shown to comprise more stable assemblages than other oligomers using molecular dynamics (MD) simulations. Models contained a predicted lumenal rimantadine binding site, as well as a second druggable target region on the membrane-exposed periphery. In silico screening enriched for repurposed drugs/compounds predicted to bind to either one site or the other. Hits displayed superior potency in vitro and in cell culture compared with rimantadine, with efficacy demonstrably linked to virion-resident channels. Finally, rimantadine effectively blocked ZIKV viraemia in preclinical models, supporting that M constitutes a physiologically relevant target. This could be explored by repurposing rimantadine, or development of new M-targeted-therapies.

Data-Driven Channel Modeling for End-to-End Visible Light DCO-OFDM Communication System Based on Experimental Data

End-to-end systems have been introduced to address the issue of independent signal processing module design in traditional communication systems, which prevents achieving global system optimization. However, research on indoor end-to-end Visible Light Communication (VLC) systems remains limited, especially regarding the channel modeling of high-speed, high-capacity Direct Current-biased Optical Orthogonal Frequency Division Multiplexing (DCO-OFDM) systems. This paper proposes three channel modeling methods for end-to-end DCO-OFDM VLC systems. The accuracy of the proposed methods is demonstrated through R-Square model fitting performance and data distribution analysis. The effectiveness of the proposed channel modeling methods is further validated by comparing the bit error rate (BER) performance of traditional receivers and existing deep learning (DL)-based receivers. The results show that the proposed methods can effectively mitigate both linear and nonlinear distortions. By employing these channel modeling methods, communication systems can reduce the spectral occupancy of pilot signals, thereby significantly lowering the complexity of traditional channel estimation methods. Thus, these methods are suitable for use in end-to-end VLC communication systems.

A robust OFDM IM-QAM NOMA scheme for URLLC and eMBB downlink service coexistence

This work introduces a non-orthogonal multiple access (NOMA) scheme suitable for low-rate ultra-reliable low-latency communication (URLLC) and enhanced mobile broadband (eMBB) services multiplexing in downlink communication. Tailored to minimize mutual interference, URLLC information is conveyed via a modified index modulation (IM) scheme on top of quadrature amplitude modulation (QAM) transferring eMBB traffic. Aiming at providing a proof of concept of the proposed scheme, we calculate the bit error rate of both services over Rayleigh fading with diversity, as well as the eMBB service achievable rate with typical multi-input multi-output (MIMO) configuration when the URLLC service utilizes space–time coding or diversity. The proposed scheme achieves a low bit error rate for the URLLC IM signal, at the cost of a lower information rate, while affecting the performance of the eMBB user mainly due to power sharing among the IM and QAM signals. To further investigate the feasibility of the proposed scheme, we calculate the transmission energy required by the base station to support both services over a typical cellular channel model, for various service requirements and user distances, in comparison to an orthogonal multiple access (OMA) puncturing scheme utilizing time-multiplexing of QAM for the eMBB traffic and BPSK for the URLLC traffic. Overall, our results show that the proposed scheme attains better performance compared to the puncturing scheme and offers a robust solution with easy user pairing for low-rate URLLC and typical eMBB downlink service multiplexing for 5G communications and beyond.