Wireless Channel Model Research Articles

Non-Stationary Wireless Channel Modeling Approach Based on Extreme Value Theory for Ultra-Reliable Communications

A proper channel modeling methodology that characterizes the statistics of extreme events is key in the design of a system at an ultra-reliable regime of operation. The strict constraint of ultra-reliability corresponds to the packet error rate (PER) in the range of $10^{-9}-10^{-5}$ within the acceptable latency on the order of milliseconds. Extreme value theory (EVT) is a robust framework for modeling the statistical behavior of extreme events in the channel data. In this paper, we propose a methodology based on EVT to model the extreme events of a non-stationary wireless channel for the ultra-reliable regime of operation. This methodology includes techniques for splitting the channel data sequence into multiple groups concerning the environmental factors causing non-stationarity, and fitting the lower tail distribution of the received power in each group to the generalized Pareto distribution (GPD). The proposed approach also consists of optimally determining the time-varying threshold over which the tail statistics are derived as a function of time, and assessing the validity of the derived Pareto model. Finally, the proposed approach chooses the best model with minimum complexity that represents the time variation behavior of the non-stationary channel data sequence. Based on the data collected within the engine compartment of Fiat Linea under various engine vibrations and driving scenarios, we demonstrate the capability of the proposed methodology in providing the best fit to the extremes of the non-stationary data. The proposed approach significantly outperforms the channel modeling approach using the stationary channel assumption in characterizing the extreme events.

Beyond 5G for digital twins of UAVs

The purpose is to explore the application effects and limitations of Unmanned Aerial Vehicle (UAV) in 5G/B5G (Beyond 5G) mobile and wireless communication. Based on 5Gcommunication, the deep learning (DL) algorithm is introduced to construct the UAV Digital Twins (DTs) communication channel model based on DL. The Coordinated Multi-point Transmission (COMP) technology is adopted to study the interference suppression of UAVs. The key algorithm in the physical layer security is employed to ensure information communication security. Finally, the model constructed is simulated and analyzed. The transmission error rates and transmission estimation accuracy of several algorithms, including the proposed algorithm and ordinary Deep Neural Networks (DNNs), are compared under different Signal-to-Noise Ratios (SNRs). Results find that the convergence speed and convergence effect of the proposed algorithm has prominent advantages, presenting strong robustness; the proposed algorithm's estimation accuracy is about 150 times higher than the traditional algorithms. Further analysis reveals that the proposed algorithm's accuracy reaches 82.39%, which increases by at least 3.2% than other classic machine algorithms. The indicators of Precision, Recall, and F1 are compared as well. Apparently, the Precision, Recall, and F1 values of the proposed algorithm are the highest, while the transmission delay is the smallest. Therefore, the constructed UAV DTs wireless communication channel model has strong robustness and further reduces UAV limitations, providing a reference for improving UAV system performance in the later stage.

Directive mmWave radio channel modeling in a ship hull

AbstractWireless connectivity has been realized for multiple environments and different frequency bands. However, little research exists about mmWave communication in industrial environments. This paper presents the 60 GHz double-directional radio channel for mmWave communication in a ship hull for Line-of-Sight (LOS) and non-Line-of-Sight (NLOS) conditions. We performed channel measurements using the Terragraph channel sounder at different locations in the ship hull and fitted LOS path loss to a one-slope path loss model. Path loss and root-mean-square delay spread of the LOS path is compared to the reflected path with lowest path loss. NLOS communication via this first-order reflected path is modeled by calculating the path distance and determining the reflection loss. The reflection losses have a considerable contribution to the signal attenuation of the reflected path. The channel models are implemented in an indoor coverage prediction tool, which was extended with a ray launching algorithm and validated by comparison with an analytical electromagnetic solver. The results show that the mmWave radio channel allows high-throughput communication within a ship hull compartment, even when no LOS path between the transmitter and receiver is present.

Wireless Channel Modelling for Identifying Six Types of Respiratory Patterns With SDR Sensing and Deep Multilayer Perceptron.

Contactless or non-invasive technology has a significant impact on healthcare applications such as the prediction of COVID-19 symptoms. Non-invasive methods are essential especially during the COVID-19 pandemic as they minimise the burden on healthcare personnel. One notable symptom of COVID-19 infection is a rapid respiratory rate, which requires constant real-time monitoring of respiratory patterns. In this paper, Software Defined Radio (SDR) based Radio-Frequency sensing technique and supervised machine learning algorithm is employed to provide a platform for detecting and monitoring various respiratory: eupnea, biot, bradypnea, sighing, tachypnea, and kussmaul. The variations in Channel State Information produced by human respiratory were utilised to identify distinct respiratory patterns using fine-grained Orthogonal Frequency-Division Multiplexing signals. The proposed platform based on the SDR and the Deep Multilayer Perceptron classifier exhibits the ability to effectively detect and classify the afore-mentioned distinct respiratory with an accuracy of up to 99%. Moreover, the effectiveness of the proposed scheme in terms of diagnosis accuracy, precision, recall, F1-score, and confusion matrix is demonstrated by comparison with a state-of-the-art machine learning classifier: Random Forest.

Clustering-Based Nonstationary Massive MIMO Channel Modeling at 1.4725 GHz

The paper reports on the radio propagation characteristics of Massive MIMO. The realistic measurements are conducted in typical outdoor LOS and NLOS scenarios with the bandwidth of 100 MHz at the carrier frequency of 1.4725 GHz. In this paper the channel propagation in spectrum and space domains are investigated by employing the high-precision parameter estimation algorithm. Based on big data technology, we propose the multipath clustering algorithm and subinterval programming to bring deeper insight into the cluster evolution over the antenna array axis. The works focus on the correlation, and the result is in accordance with the theory of the cluster’s visible region. Furthermore, a non-WSSUS (non-wide sense stationary uncorrelated scattering) channel analytical model is established. The whole research work aims to contribute the radio channel modeling of the 5G Massive MIMO communication system.

BER PERFORMANCE OF JOINT POLAR CODED SIGNAL AND MIMO USING MILLIMETER WAVE

There have been active researches worldwide for evolving the next-generation networks such as 5G. The 5G network is predicted to ensure considerable mobile data traffic having large number of wireless connections. This is combined with high energy-efficiency and Quality of Service (QoS). For these reasons, 5G should be exploited the prospect of new developments, including multiple-input multiple-output (MIMO) technology and using higher frequencies such as the millimeter-wave (mmWave) frequency band. Error correction techniques are commonly utilized to reduce transmission error and enhance QoS. In the present work, Cyclic Redundancy Check (CRC) aided polar codes with successive cancellation list (SCL) decoding is used in conjunction with mmWave and MIMO system in order to gain the advantages of these techniques together. The mentioned combined techniques are modeled and simulated to show their performances over different wireless channel models mainly: Additive White Gaussian Noise (AWGN), flat fading, and mmWave indoor fading channel. The results showed that combining polar code with MIMO system improves the Bit Error Rate (BER) performance by at least 6 dB at BER of 10-4 compared to the case where no polar code being used.

Wireless Power Transfer for Aircraft IoT Applications: System Design and Measurements

Sensors currently deployed on board have wired connectivity, which increases weight and maintenance costs for aircraft. Removing cables for wireless communications of sensors on board alleviates the cost, however, the powering of sensors becomes a challenge inside aircraft. Wireless power transfer (WPT) via radio-frequency (RF) signals is an emerging solution to remotely power sensors for battery-less operation with long-lived capacitors. In this article, we design a WPT system for aircraft IoT-type applications, including low data rate inside (LI) sensors by determining the number, location, and tilt angles of WPT transmitters given constraints based on the cabin geometry and duty cycle of the sensors. We formulate a robust optimization problem to address the WPT system design under channel uncertainties. We also derive an equivalent integer linear programming and solve that for an optimal deployment to satisfy the duty cycle requirements of LI sensors. We perform experiments inside the cabin to validate the wireless avionics intracommunications channel model. Our simulations demonstrate the feasibility of 90% robust design with 14 WPT transmitters for duty cycles less than 0.1% while keeping the human radiation exposure below the recommended reference value of 4.57 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .

Optimal Resource Allocations for Statistical QoS Provisioning to Support mURLLC Over FBC-EH-Based 6G THz Wireless Nano-Networks

One of most important techniques for enabling the sixth-generation (6G) mobile wireless network lies in how to efficiently guarantee various stringent quality-of-service (QoS) performance-metrics to support the emerging massive Ultra-Reliable Low-Latency Communications (mURLLC) in 6G. Correspondingly, finite blocklength coding (FBC) has been developed as an effective technique to significantly improve various QoS indices for mURLLC through implementing short-packet communications. On the other hand, Terahertz (THz) band wireless nano-communications have been widely envisioned as a promising 6G technique to efficiently support utra-high data-rate (up to 1 Tbps). One of the major constraints over THz-band nano-networks is the severely limited energy that can be accessed by nano devices. Towards this end, various novel energy harvesting (EH) mechanisms have been proposed to remedy the energy scarcity problem. However, how to accurately characterize the relationships among THz wireless channels, energy consumption, and EH models for FBC based nano communications remains a challenging problem to support statistical delay and error-rate bounded QoS provisioning over FBC based 6G THz wireless nano-networks. To overcome these challenges, in this paper we propose optimal resource allocation policies to achieve the maximum ε-effective capacity in the THz band over FBC-EH-based nano-networks. Particularly, we establish nano-scale system models and characterize wireless channel models in the THz band using FBC. In order to support statistical delay and error-rate bounded QoS provisioning, we formulate and solve the ε-effective capacity maximization problem under several different EH constraints for our proposed schemes. Simulation results are included, which validate and evaluate our proposed schemes in the finite blocklength regime.

Wireless Channel Modeling Based on Machine Learning in the High-speed Railway Scenario

In the high-speed railway (HSR) scenario, there is a problem of poor generalization ability caused by the overfitting phenomenon through wireless channel modeling. In this paper, a precise modeling method based on machine learning is proposed to address this issue under the wireless channel of HSR. According to the K-means clustering algorithm, the typical values of the K-factor in a multitude of scenarios are obtained through Rician K-factor clustering in the wireless channel fading model. Founded on the theory of model evaluation and selection in machine learning, the measured values of path loss are fitted by least squares regression using the cross-validation method. Then, we obtain the formulas of the path loss models and the expected generalization errors of the models in different scenarios. The mathematical relationship of the ergodic capacity depending on the Rician K-factor, the signal-noise ratio of the receiver, and the path loss generalization error are established after the ergodic capacity analysis of the HSR wireless channel. Finally, the lower boundary curves of the ergodic capacity are obtained by simulation experiments in different scenarios.

Analysis of the Dominant Signal Component of the Air-Ground Channel Based on Measurement Data at C-Band

Operating remotely piloted aircraft is not imaginable without a continuous data exchange between the air vehicle and the remote pilot. This data exchange requires reliable data links. One approach for such a data link discussed in the community is a terrestrial system deployed in C-band. A good knowledge of the physical conditions of the communication channel, in this case the air-ground/ground-air channel, is indispensable for the development of wireless data links. Therefore we carried out a 50 MHz bandwidth channel sounding campaign with a terrestrial transmitter and an airborne receiver. In this paper we give a detailed description of our campaign setup and the processing of the collected data. The campaign covered several flight scenarios, such as take-off, taxiing, and multiple en-route maneuvers. We furthermore present results on the received power and the amplitude distribution of the dominant component of the received signal for the different flight scenarios. We observed significant drops in reception power during certain maneuvers that need to be considered in the design process of a data link for unmanned aviation. Additionally, we show that the amplitude distribution follows the distributions commonly used in statistical channel modeling of wireless channels to some extent. We finally present parameter sets for multiple flight scenarios for scaling the amplitude distributions to allow a statistical channel modeling of the reception power of the first resolvable signal path.

Massive OAM‐MIMO transmission scheme for 5G networks and beyond

The combination of orbital angular momentum (OAM) and massive multiple-input multiple-output (MIMO) brings the possibility of almost infinite transmission capability, which may be applied in the coming 5th generation networks and beyond. In this study, a uniform line array (ULA)-based massive OAM-MIMO scheme in a multipath scene is presented. The cumulative phase of OAM carriers and multiple reference coordinate systems relative to each transmitting antenna are used to describe the OAM-MIMO wireless channel model theoretically. The performance indices of interest including capacity, energy efficiency, and capacity gain are numerically investigated in detail. The results indicate that the presented ULA-based massive OAM-MIMO scheme performs better overall than a conventional MIMO scheme via enhancing the OAM multiplexing intensity, and multi-fold capacity gain can be obtained especially in the low signal-to-noise rate communications.

Tensor Decompositions in Wireless Communications and MIMO Radar

The emergence of big data and the multidimensional nature of wireless communication signals present significant opportunities for exploiting the versatility of tensor decompositions in associated data analysis and signal processing. The uniqueness of tensor decompositions, unlike matrix-based methods, can be guaranteed under very mild and natural conditions. Harnessing the power of multilinear algebra through tensor analysis in wireless signal processing, channel modeling, and parametric channel estimation provides greater flexibility in the choice of constraints on data properties and permits extraction of more general latent data components than matrix-based methods. Tensor analysis has also found applications in Multiple-Input Multiple-Output (MIMO) radar because of its ability to exploit the inherent higher-dimensional signal structures therein. In this paper, we provide a broad overview of tensor analysis in wireless communications and MIMO radar. More specifically, we cover topics including basic tensor operations, common tensor decompositions via canonical polyadic and Tucker factorization models, wireless communications applications ranging from blind symbol recovery to channel parameter estimation, and transmit beamspace design and target parameter estimation in MIMO radar.

Radio Channel Modeling in a Ship Hull: Path Loss at 868 MHz and 2.4, 5.25, and 60 GHz

This letter presents the analysis of a channel sounding campaign in the hull of a bulk carrier vessel at sub-6 GHz frequencies 868 MHz and 2.4 and 5.25 GHz as well as millimeter-wave (mmWave) frequency 60.48 GHz. For the sub-6 GHz frequencies, path loss (PL) is measured using omnidirectional antennas. The measured PL is fitted to a one-slope PL model that takes into account attenuation due to engine room floor crossings, obstructions, and walls. The fitted model has a reference PL lower than free space and a PL exponent around 1.2. For mmWave wireless communication, high gain directive antennas are typically used. Therefore, the directive radio channel for line-of-sight (LOS) communication is measured using the Terragraph channel sounder and an antenna beamwidth dependent PL model is created. At mmWave frequencies, LOS PL in the vessel is close to PL in a free-space environment, but angular spread values are lower compared to other indoor scenarios.

Performance study of path loss models at 14, 18, and 22 GHz in an indoor corridor environment for wireless communications

The critical rule to achieve extremely high peaks of data transmission is the availability of a tremendous amount of bandwidth. The super high frequency (SHF) and the millimeter-wave (mmWave) frequency bands are the candidates for the deployment of the 5G cellular system and for satisfying future needs due to their massively available blocks of contiguous raw bandwidth that is capable of supporting additional data traffic for multimedia services. This research paper presents propagation measurements at three frequencies above 6 GHz, which are 14, 18, and 22 GHz frequency bands. The measurements were carried out or both line-of-sight (LOS) and non-line-of-sight (NLOS) communication scenarios in an indoor corridor environment to present frequency- and distance-dependent wireless channel models. Moreover, this study presents, investigates, and compares the performance of two well-known path loss prediction models; the single frequency close-in (CI) free space reference distance model and the single frequency floating intercept (FI) model. The LOS comparison study shows that the CI and FI models provide comparable and accurate estimates that fit the real measured data for the frequency bands selected. Furthermore, the study investigates the behavior of the path loss exponent (PLE) and the FI model parameters as a function of the reception angle of arrival (AoA) in the NLOS scenario. It is observed from this work that the path loss models' parameters exhibit symmetrical behavior around 180° AoA. The FI model provides the same standard deviation values as the CI model in the LOS scenario. In contrast, the FI model offers a notable reduction(up to 2.84 dB) compared to the CI model in the NLOS scenario. Finally, the LOS and NLOS results reveal that the CI and FI models can be trusted as good path loss models for corridor environments and exhibit stable behavior over measured distances and frequencies.

Increasing energy efficiency of Massive-MIMO network via base stations switching using reinforcement learning and radio environment maps

Energy Efficiency (EE) is of high importance while considering Massive Multiple-Input Multiple-Output (M-MIMO) networks where base stations (BSs) are equipped with an antenna array composed of up to hundreds of elements. M-MIMO transmission, although highly spectrally efficient, results in high energy consumption growing with the number of antennas. This paper investigates EE improvement through switching on/off underutilized BSs. It is proposed to use the location-aware approach, where data about an optimal active BSs set is stored in a Radio Environment Map (REM). For efficient acquisition, processing and utilization of the REM data, reinforcement learning (RL) algorithms are used. State-of-the-art exploration/exploitation methods including ϵ-greedy, Upper Confidence Bound (UCB), and Gradient Bandit are evaluated. Then analytical action filtering, and an REM-based Exploration Algorithm (REM-EA) are proposed to improve the RL convergence time. Algorithms are evaluated using an advanced, system-level simulator of an M-MIMO Heterogeneous Network (HetNet) utilizing an accurate 3D-ray-tracing radio channel model. The proposed RL-based BSs switching algorithm is proven to provide 70% gains in EE over a state-of-the-art algorithm using an analytical heuristic. Moreover, the proposed action filtering and REM-EA can reduce RL convergence time in relation to the best-performing state-of-the-art exploration method by 60% and 83%, respectively.

Optimum Device and Modulation Scheme Selection for Optical Wireless Communications

There has been an extensive modelling of the optical wireless channel, and the optimum modulation scheme for a particular channel is well-understood. However, this modelling has not taken into account the trade-offs that transmitter and receiver selection usually involve. For a particular type of transmitter, the modulation bandwidth and available power are closely related, as are receiver bandwidth, active area and sensitivity. In this article, we present a design approach that takes this device selection into account. The article details a general design method for an optical wireless communication system using a holistic design approach (i.e., considering channel, modulation schemes, and device constraints). The article shows results for particular examples, showing a substantial increase in margin (or data-rate) is available using this approach. For instance, by using this approach mutually optimising both modulation schemes and device constraints, it is found that for an optimally chosen Gallium Nitride micro-LED and a commercial photo receiver pair, a 20 dB SNR margin (or ∼3 times data-rate improvement) can be obtained compared with a more typical approach mainly concerning the modulation scheme optimisation.

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

In this paper, a novel three-dimensional (3D) non-stationary geometry-based stochastic model (GBSM) for the fifth generation (5G) and beyond 5G (B5G) systems is proposed. The proposed B5G channel model (B5GCM) is designed to capture various channel characteristics in (B)5G systems such as space-time-frequency (STF) non-stationarity, spherical wavefront (SWF), high delay resolution, time-variant velocities and directions of motion of the transmitter, receiver, and scatterers, spatial consistency, etc. By combining different channel properties into a general channel model framework, the proposed B5GCM is able to be applied to multiple frequency bands and multiple scenarios, including massive multiple-input multiple-output (MIMO), vehicle-to-vehicle (V2V), high-speed train (HST), and millimeter wave-terahertz (mmWave-THz) communication scenarios. Key statistics of the proposed B5GCM are obtained and compared with those of standard 5G channel models and corresponding measurement data, showing the generalization and usefulness of the proposed model.

Reinforcement Learning for Energy-Efficient 5G Massive MIMO: Intelligent Antenna Switching

To provide users with high throughputs, the fifth generation (5G) and beyond networks are expected to utilize the Massive Multiple-Input Multiple-Output technology (MMIMO), i.e., large antenna arrays. However, additional antennas require the installation of dedicated hardware. As a result the power consumption of a 5G MMIMO network grows. This implies, e.g., higher operator costs. From this angle, the improvement of Energy Efficiency (EE) is identified as one of the key challenges for the 5G and beyond networks. EE can be improved through intelligent antenna switching, i.e., disabling some of the antennas installed at a 5G MMIMO Base Station (BS) when there are few User Equipments (UEs) within the cell area. To improve EE in this scenario we propose to utilize a sub-class of Machine Learning techniques named Reinforcement Learning (RL). Because 5G and beyond networks are expected to come with accurate UE localization, the proposed RL algorithm is based on UE location information stored in an intelligent database named a Radio Environment Map (REM). Two approaches are proposed: first EE is maximized independently for every set of UEs’ positions. After that the process of learning is accelerated by exploiting similarities between data in REM, i.e., REM-Empowered Action Selection Algorithm (REASA) is proposed. The proposed RL algorithms are evaluated with the use of a realistic simulator of the 5G MMIMO network utilizing an accurate 3D-Ray-Tracing radio channel model. The utilization of RL provides about 18.5% EE gains over algorithms based on standard optimization methods. Moreover, when REASA is used the process of learning can be accomplished approximately two times faster.

Research on stability modelling of high speed electronic communication based on hierarchical optimal mining algorithms

In order to solve the problems of low accuracy, bad network topological data integrity and low communication transmission efficiency in the current stability modelling methods for high-speed electronic communication, a new stability modelling method for high-speed electronic communication based on hierarchical optimisation mining algorithm is proposed. This paper analyses the transmission characteristics of high-speed signals in the process of electronic communication, constructs a wireless channel model by using hierarchical optimisation mining algorithm, and carries out distance correspondence based on the wireless channel model. The stability of high-speed electronic communication is modelled and analysed by using one-step transition probability matrix, node relative distribution and link duration. Experimental results show that this method has the advantages of high model accuracy, good topological data integrity and high communication transmission efficiency.

Development of a Method for Boundary Determination of the Noise-resistant Area of the UHF/VHF Band

A method is proposed for determining the boundaries of the maximum largest zone of stable radio reception in the UHF/VHF range using beam antenna system under the action of several sources of radio interference located at different altitudes. The increase in the size of the stable radio reception area is ensured by the optimal orientation of the antenna arrangement at each point in azimuth and angular altitude using the radio communication channel model. To determine the coordinates of the map points belonging to the boundary, a modification of the wave algorithm is proposed. The conducted experimental studies for the MototrboTM DP4800 and Kenwood TK-8302 radio equipment allow concluding that the theoretical positions and computer simulation analysis results obtained are reliable.