Wireless Channel Model Research Articles

Wireless Channel Measurement and Modeling in Industrial Environments

Wireless Channel Measurement and Modeling in Industrial Environments

On the impact of the physical layer model on the performance of D2D-offloading in vehicular environments

Offloading data traffic from Infrastructure-to-Device (I2D) to Device-to-Device (D2D) communications is a powerful tool for reducing congestion, energy consumption, and spectrum usage of mobile cellular networks. Prior network-level studies on D2D data offloading focus on high level performance metrics as the offloading efficiency, and take into account the radio propagation aspects by using simplistic wireless channel models. In this work, we consider a D2D data offloading protocol tailored to highly dynamic scenarios as vehicular environments, and evaluate its performance focusing on physical layer aspects, like energy consumption and spectral efficiency. In doing this, we take into account more realistic models of the wireless channel, with respect to the simplistic ones generally used in the previous studies. Our objective is twofold: first, to quantify the performance gain of the considered D2D offloading protocol with respect to a classic cellular network, based on I2D communications, in terms of energy consumption and spectral efficiency. Second, to show that using simplistic channel models may prevent to accurately evaluate the performance gain. Additionally, the use of more elaborated models allows to obtain insightful information on relevant system-level parameters settings, which would not be possible to obtain by using simple models. The considered channel models range from widely used models based on deterministic path loss formulas, to more accurate ones, which include effects like multipath fading and the associated frequency selectivity of wideband channels. These models have been proposed and validated, in the recent years, through large-scale measurements campaigns.Our results show that the considered protocol is able to achieve a reduction in the energy consumption of up to 35%, and an increase in the system spectral efficiency of 50%, with respect to the benchmark cellular system. The use of different channel models in evaluating these metrics may result, in the worst case, in a sixfold underestimation of the achieved improvement.

Narrowband Characterization of Near-Ground Radio Channel for Wireless Sensors Networks at 5G-IoT Bands.

In this contribution, a narrowband radio channel model is proposed for rural scenarios in which the radio link operates under near-ground conditions for application in wireless sensor networks dedicated to smart agriculture. The received power attenuation was measured for both transmitter and receiver antennas placed at two different heights above ground: 0.2 and 0.4 m. Three frequency ranges, proposed for future 5G-IoT use case in agriculture, were chosen: 868 MHz, 2.4 GHz and 5.8 GHz. Three ground coverings were tested in a rural scenario: soil, short and tall grass fields. The path loss was then estimated as dependent of the radio link range and a three-slope log-normal path loss model was tailored. Results are explained in terms of the first Fresnel zone obstruction. Commercial Zigbee sensor nodes operating at 2.4 GHz were used in a second experiment to estimate the link quality from the experimental Radio Signal Strength Indicator (RSSI) received values. Two sensor nodes were placed at the same elevation above ground as in the previous experiment, only for short grass field case. The Quality of Service performance was determined in terms of theoretical bit error rate achieved for different digital modulations—BPSK, 8PSK and 16QAM—concluding remarkable results for an obstructed radio link.

A General 3-D Non-Stationary 5G Wireless Channel Model

A novel unified framework of geometry-based stochastic models for the fifth generation (5G) wireless communication systems is proposed in this paper. The proposed general 5G channel model aims at capturing small-scale fading channel characteristics of key 5G communication scenarios, such as massive multiple-input multiple-output, high-speed train, vehicle-to-vehicle, and millimeter wave communications. It is a 3-D non-stationary channel model based on the WINNER II and Saleh-Valenzuela channel models considering array-time cluster evolution. Moreover, it can easily be reduced to various simplified channel models by properly adjusting model parameters. Statistical properties of the proposed general 5G small-scale fading channel model are investigated to demonstrate its capability of capturing channel characteristics of various scenarios, with excellent fitting to some corresponding channel measurements.

An extension of the RiMAX multipath estimation algorithm for ultra-wideband channel modeling

This work presents an extension of the high-resolution RiMAX multipath estimation algorithm, enabling the analysis of frequency-dependent propagation parameters for ultra-wideband (UWB) channel modeling. Since RiMAX is a narrowband algorithm, it does not account for the frequency-dependency of the radio channel or the environment. As such, the impact of certain materials in which these systems operate can no longer be considered constant with respect to frequency, preventing an accurate estimation of multipath parameters for UWB communication. In order to track both the specular and dense multipath components (SMC and DMC) over frequency, an extension to the RiMAX algorithm was developed that can process UWB measurement data. The advantage of our approach is that geometrical propagation parameters do not appear or disappear from one sub-band onto the next. The UWB-RiMAX algorithm makes it possible to re-evaluate common radio channel parameters for DMC in the wideband scenario, and to extend the well-known deterministic propagation model comprising of SMC alone, towards a more hybrid model containing the stochastic contributions from the DMC’s distributed diffuse scattering as well.Our algorithm was tested with synthetic radio channel models in an indoor environment, which show that our algorithm can match up to 99% of the SMC parameters according to the multipath component distance (MCD) metric and that the DMC reverberation time known from the theory of room electromagnetics can be estimated on average with an error margin of less than 2 ns throughout the UWB frequency band. We also present some preliminary results in an indoor environment, which indicate a strong presence of DMC and thus diffuse scattering. The DMC power represents up to 50% of the total measured power for the lower UWB frequencies and reduces to around 30% for the higher UWB frequencies.

Energy Harvesting over Rician Fading Channel: A Performance Analysis for Half-Duplex Bidirectional Sensor Networks under Hardware Impairments.

In this paper, a rigorous analysis of the performance of time-switching energy harvesting strategy that is applied for a half-duplex bidirectional wireless sensor network with intermediate relay over a Rician fading channel is presented to provide the exact-form expressions of the outage probability, achievable throughput and the symbol-error-rate (SER) of the system under the hardware impairment condition. Using the proposed probabilistic models for wireless channels between mobile nodes as well as for the hardware noises, we derive the outage probability of the system, and then the throughput and SER can be obtained as a result. Both exact analysis and asymptotic analysis at high signal-power-to-noise-ratio regime are provided. Monte Carlo simulation is also conducted to verify the analysis. This work confirms the effectiveness of energy harvesting applied in wireless sensor networks over a Rician fading channel, and can provide an insightful understanding about the effect of various parameters on the system performance.

Inside view

Researchers from the Sun Yat-sen University in China have presented a new transmission scheme for the Internet of Vehicles to recover information from simultaneously broadcast signals that are overlapped. We spoke to Professor Xiao Ma, one of the authors, to find out more about his and his team's latest work. My research interests include channel coding, information theory, and their applications. While our mobile devices are becoming smaller but providing more functions, people require more reliable transmission to guarantee real-time demands. On the other hand, more and more smart devices will access the Internet. It becomes a challenge to build a network framework to utilise the network resource fully. I was very interested in developing superposition coding and transmission which provides an efficient way to utilise channel resource and gain more performance. Professor Xiao Ma from Sun Yat-sen University in China The proposed decoding algorithm SCL-based IEDA (red) performs well compared to other popular algorithms Internet of Vehicles (IoV) can be seen as a special Internet of Things (IoT). Using information interaction among vehicles, humans and the roadside unite. IoV helps people to collect real-time traffic information and gain more convenience and comfort. Since more and more cars and other vehicles are used daily by people, a large number of devices require access to the IoVs. Different from traditional IoT communication, IoV is deployed to transmit rapidly and reliably the road condition information (e.g. traffic jam notifications) in most cases. With IoV communication, we can build autonomous vehicles and improve travel safety and convenience. In the non-orthogonal overlapping transmission system in this Letter, we use power allocation to distinguish two signals from different relay nodes. In the destination node, we present a successive cancellation list based interference elimination decoding algorithm (SCL-based IEDA) for information recovery. Instead of outputting a single pair of estimated information sequences using a traditional SCL decoder, our decoding algorithm outputs more alternative pairs of estimated information sequences and picks up the most probable one. The significance of our work is that we can recover information from the overlapped signals by the presented decoding algorithms, even if the signals are highly overlapped in the same time slot and frequency band. By power allocation, we distinguish the signals so that the information in the highly overlapped signals is recoverable. Our scheme can not only be applied to the mentioned application scenario in this paper, but also be extended to other many-to-one communication networks to improve the throughput of the network. The long term impacts of this work mainly include: 1) With polar code being selected for the 5G standard, this work will attract more people to pay attention to polar code and its application in multi-user transmission systems. 2) The Internet of Vehicles is regarded as one of the typical 5G application scenarios of URLLC, which requires a transmission with high reliability and low latency. The polar code-based overlapping transmission scheme may find applications in improving the transmission efficiency in IoVs. Our further plans for developing this work include：1) Try to improve the decoding algorithm of the proposed overlapping transmission. 2) Consider the case when more than two signals from different relay nodes are overlapped and study the corresponding decoding algorithms for information recovery. 3) Conduct simulations under a more complex and practical wireless channel model to further investigate the information recovery performance of the proposed decoding algorithm. Currently, we are actively working on the above mentioned plans. As we all know, dedicated short range communications were used for vehicle communications in the US. On the other hand, EN 202 663 was used in Europe. However, due to the limited range and low speed transmission, research attempts have been made on cellular based Vehicle to Everything (C-V2X) communications. The benefits of C-V2X communication, compared to prior technologies, are that it can accommodate more users, provide faster transmission speed, and is of lower latency performance. As part of the evolution of cellular communications, the forthcoming fifth generation, V2X (5G-V2X), communications will further leverage this trend. In the coming ten years, it is believed that along with new energy resources, automatic driving, accurate image detection, and three-dimensional scene reconstruction technologies, C-V2X will further leverage the driving experience and rebuild the car industry. Although C-V2X has been widely discussed in literature, there is still a lot of work that needs to be done before its application, for instance, frequency bandwidth allocation and its effective usage, precise vehicle transmission channel measurement and modelling, seamless wireless connection for massive connected devices (vehicles, pedestrians, etc.), URLLC, even fast speed transmissions for in-car entertainment, vehicle network security and flexible network architecture for fast moving vehicles.

Efficient decoding algorithm for 3D video over wireless channels

In wireless communication networks, additive channel noise and time varying multipath fading are considered amongst the main challenges and these effects will bring significant quality degradation to the transmitted 2D/3D video streams. This paper presents an efficient decoding algorithm for Distributed Video Coding (DVC) for enhanced performance of colour and depth based 3D video over error prone wireless channels. The maximum a-posteriori (MAP) algorithm for turbo decoding is modified considering the effects of the channel errors on both Wyner-Ziv and key frame bit streams of colour and depth videos. The proposed codec is simulated using a W-CDMA wireless channel model and the results are analyzed to determine the effect of the modifications. The performance of the state-of-the-art H.264/AVC video codec is also presented for comparison under similar conditions. The results show that the proposed modifications provide a significant improvement in the DVC codec performance under unfavourable channel conditions for colour plus depth based 3D video.

High‐speed based adaptive beamforming handover scheme in LTE‐R

In recent years, high-speed railways (HSRs) are being developed rapidly all over the world because of their convenience, safety, comfort, and other advantages. However, the reliability and security of HSR wireless communication systems have faced severe challenges, such as the Doppler effect, complicated wireless channel model, frequent handovers and so on. The International Union of Railways is pushing the evolution of the global system for mobile communications for railway (GSM-R) internationally. In this study, a high-speed based adaptive beamforming handover scheme is proposed to improve the handover performance for HSR wireless communication systems. When the high-speed train enters the overlapping region, the serving evolved NodeB (eNodeB) and target eNodeB are using beamforming with different gain factors to improve the received signal quality. In addition, this scheme can dynamically adjust the handover hysteresis margins of the reference signal receiving power (RSRP) and the reference signal receiving quality (RSRQ) based on the speed and position. Simulation results have demonstrated that the proposed handover scheme can improve the handover performance by increasing the handover trigger probability and success probability effectively.

Clustering Enabled Wireless Channel Modeling Using Big Data Algorithms

Recently, rapid growth in data services has ushered in the so-called big data era, and data mining and analysis techniques have been widely adopted to extract value from data for different applications. Channel modeling also benefits in this era, in particular by exploiting algorithmic techniques developed for big data applications. In this article, the challenges and opportunities in clustering-enabled wireless channel modeling are discussed in this context. First, some well known clustering techniques, which are potentially capable of enabling clustered channel modeling, are presented. Next, the motivation of cluster-based channel modeling is presented. The typical concepts of clusters used in channel models are summarized, and the state-of-the-art clustering and tracking algorithms are reviewed and compared. Finally, several promising research problems for channel clustering are highlighted.

Analyses of the Incomplete Low-Bit-Rate Hybrid PLC-Wireless Single-Relay Channel

This paper discusses the ergodic achievable data rate of the so-called incomplete hybrid power line-wireless single-relay channel (HSRC) model. Here, the term incomplete refers to the HSRC model, which jointly and in parallel uses power line and wireless single-relay channel (SRC) models, for low-bit-rate data transmission, without a data communication link or a node communication interface. In order to analyze the behavior of such kind of defective HSRC model under the relative location of the relay node in relation to the source and destination nodes, optimal power allocation is taken into account under a sum power constraint with an amplify-and-forward relaying protocol and maximal ratio combining applied at the relay node. Analytical and numerical results show that the incomplete HSRC model, which is a defective version of the HSRC model, can remarkably outperform power line and wireless SRCs for all considered positions of the relay node and the chosen cooperative protocols.

Nanoscale Optical Wireless Channel Model for Intra-Body Communications: Geometrical, Time, and Frequency Domain Analyses

In vivo wireless nanosensor networks (iWNSNs) consist of communicating miniature devices with unprecedented sensing and actuation capabilities, which are able to operate inside the human body. iWNSNs are the basis of emerging healthcare applications, such as intrabody health-monitoring and control of biological processes at subcellular level. Major progress in the field of nanoelectronics, nanophotonics, and wireless communication is enabling the interconnection of the nanodevices in iWNSNs. In this paper, the effect of single biological cells and cell assemblies on the propagation of optical wave for intrabody communications of nanosensors is analytically investigated in three distinct ways, namely, geometrical, time-domain, and frequency-domain analyses. The analytical channel model is validated by means of full wave electromagnetic simulations through a case study for red blood cells (RBCs) inside the blood plasma. The results show that RBCs perform as optical microlenses that confine the radiated light on a focal area, which agrees with recent experimental achievements. It is also shown that changes in shape and size of the cells slightly alter the channel impulse response. This study motivates the development of new communication solutions for intrabody nanoscale optical communication networks and new nanobiosensing strategies able to identify diseases which cause cell shape alterations.

Construction and analysis of multi-path propagation model for indoor short range Ultra-wideband signal based on time domain ray tracing method

The realization of the Internet of Things can not be separated from the wireless sensor network which was the extension of communication and perception. The shortage of spectrum resources will become one of the bottlenecks that hinder the rapid development of Internet of Things. A large number of sensor communication makes the interference problem of wireless channel more and more serious. Therefore, the research of wireless channel characteristics and model was important for development of Internet of Things. In this paper, the time domain multipath propagation characteristics of direct, reflection, diffraction and transmission were introduced, which provided a more accurate basis for the design of indoor short distance Ultra-wideband (UWB) communication model. Considering the practicability of simulation, the multipath propagation characteristics of UWB signals under indoor short distance environment with wooden furniture were studied. Moreover, Aluminium door partitions, glass partitions, and plastic furnitures were also considered during our simulation. Based on the time domain ray tracing method, the UWB signal propagation characteristics such as received power distribution, root mean square delay spread, multipath arrive delay distribution parameters were also discussed under line-of-sight and non-line-of-sight propagation. The analysis results provided deployment basis of UWB signal under the indoor environment, which benefit to research and develop technology for indoor Internet of Things.

PHY-Layer Authentication With Multiple Landmarks With Reduced Overhead

Physical (PHY)-layer authentication systems can exploit channel state information of radio transmitters to detect spoofing attacks in wireless networks. The use of multiple landmarks each with multiple antennas enhances the spatial resolution of radio transmitters, and thus improves the spoofing detection accuracy of PHY-layer authentication. Unlike most existing PHY-layer authentication schemes that apply hypothesis tests and rely on the known radio channel model, we propose a logistic regression-based authentication to remove the assumption on the known channel model, and thus be applicable to more generic wireless networks. The Frank-Wolfe algorithm is used to estimate the parameters of the logistic regression model, in which the convex problem under a ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm constraint is solved for weight sparsity to avoid over-fitting in the learning process. We design a distributed Frank-Wolfe-based PHY-layer authentication to further reduce the communication overhead between the landmarks and the security agent. Then, we construct an incremental aggregated gradient-based scheme to provide online authentication with a higher accuracy and lower computation overhead. Simulation and experimental results validate the accuracy of the proposed authentication schemes, and show the reduced communication and computation overheads.

Sea Turtles Geolocalization in the Indian Ocean: An Over Sea Radio Channel framework integrating a trilateration technique

This paper deals with the modeling of the over sea radio channel and aims to establish sea turtles localization off the coast of Reunion Island, and also on Europa Island in the Mozambique Channel. In order to model this radio channel, a framework measurement protocol is proposed. The over sea measured channel is integrated to the localization algorithm to estimate the turtle trajectory based on Power of Arrival (PoA) technique compared to GPS localization. Moreover, cross correlation tool is used to characterize the over sea propagation channel. First measurement of the radio channel on the Reunion Island coast combine to the POA algorithm show an error of 18 m for 45% of the approximated points.

Assessment of Path Loss Prediction Models for Wireless Propagation Channels at L-Band Frequency over Different Micro-Cellular Environments of Ekiti State, Southwestern Nigeria

Assessment of Path Loss Prediction Models for Wireless Propagation Channels at L-Band Frequency over Different Micro-Cellular Environments of Ekiti State, Southwestern Nigeria

Mobile Crowdsensing Games in Vehicular Networks

Vehicular crowdsensing takes advantage of the mobility of vehicles to provide location-based services in large-scale areas. In this paper, mobile crowdsensing (MCS) in vehicular networks is analyzed and the interactions between a crowdsensing server and vehicles equipped with sensors in an area of interest is formulated as a vehicular crowdsensing game. Each participating vehicle chooses its sensing strategy based on the sensing cost, radio channel state, and the expected payment. The MCS server evaluates the accuracy of each sensing report and pays the vehicle accordingly. The Nash equilibrium of the static vehicular crowdsensing game is derived for both accumulative sensing tasks and best-quality sensing tasks, showing the tradeoff between the sensing accuracy and the overall payment by the MCS server. A Q-learning-based MCS payment strategy and sensing strategy is proposed for the dynamic vehicular crowdsensing game, and a postdecision state learning technique is applied to exploit the known radio channel model to accelerate the learning speed of each vehicle. Simulations based on a Markov-chain channel model are performed to verify the efficiency of the proposed MCS system, showing that it outperforms the benchmark MCS system in terms of the average utility, the sensing accuracy, and the energy consumption of the vehicles.

Performance Analysis and Evaluation for Active Antenna Arrays Under Three-Dimensional Wireless Channel Model

In this paper, we establish a full 3-D channel model to support the performance analysis and evaluation of active antenna array (AAA)-based wireless communication systems. We analyze and compare the impact of three different downtilt methods employed in AAA antennas, electrical downtilt (ET), mechanical downtilt (MT), hybrid downtilt (the combination of ET and MT), on the antenna patterns, which would notably impact the performance of mobile wireless communication systems. We compare the performances of the wireless communication system throughput based on the 2-D and 3-D wireless channel models using passive antenna arrays. We also investigate the system performance in terms of the capacity and coverage with different AAAs under the 3-D channel. After the performance analysis and evaluation, we have observed that the downtilt optimization may introduce significant gains in coverage and capacity for individual antennas with smaller beamwidth of the vertical patterns, but may not lead to notable gains for individual antennas with relative larger beamwidth of the vertical patterns.

A Survey on Machine Learning‐Based Mobile Big Data Analysis: Challenges and Applications

This paper attempts to identify the requirement and the development of machine learning‐based mobile big data (MBD) analysis through discussing the insights of challenges in the mobile big data. Furthermore, it reviews the state‐of‐the‐art applications of data analysis in the area of MBD. Firstly, we introduce the development of MBD. Secondly, the frequently applied data analysis methods are reviewed. Three typical applications of MBD analysis, namely, wireless channel modeling, human online and offline behavior analysis, and speech recognition in the Internet of Vehicles, are introduced, respectively. Finally, we summarize the main challenges and future development directions of mobile big data analysis.

Measurement and Analysis of Wireless propagative Model of 433MHz and 2.4GHz Frequency in Southern China Orchards

Wireless Sensor network (WSN) has great advantage in monitoring agriculture environment, but there is different propagation characteristic in various circumstance of agriculture planting such as landform, climate and radio propagation path. Recent researches rarely give sufficient consideration to various wireless propagation circumstances, especially rain environment; their wireless channel model can only describe a specific situation. In this paper, WX2530 and WX1110 were selected to measure the radio propagation characteristic of 2.4GHz and 433MHz frequency. Two WSN nodes were deployed in the hillside orchard of South China Agriculture University (SCAU) in southern China, and the Receiver Signal Strength Index (RSSI) results are recorded under different circumstances including Light of Sight (LOS) and Non-Light of Sight (NLOS) propagation path, slope landform, sunny and rainy day. A new model including path factor and rain attenuation factor is proposed, the wireless transmission fitting equation of different circumstance are derived based on the measured data, these equations match the measured data well and their related coefficients of determination R2 are bigger than 0.919. New wireless propagative models could be used to guidance the deployment and transmission of plant information, improve the monitoring efficiency of hillside orchard in southern China.