Evaluation of mmWave 5G Performance by Advanced Ray Tracing Techniques

Technological progress leads to the emergence of new concepts, which can change people’s everyday lives and accelerate the transformation of many industries. Among the more recent of these revolutionary concepts are big data analysis, artificial intelligence, augmented/virtual reality, quantum computing, and autonomous vehicles. However, this list would be incomplete without referring to fifth-generation (5G) technology, which is driven by several trends. First, the exponential growth of the worldwide monthly smartphone traffic up to 50 petabytes during the next three years will require the development of mobile networks supporting high datasharing capabilities, excellent spectral efficiency, and gigabits per second of throughput. Another trend is Industry 4.0/5.0 (also called the smart factory), which refers to advanced levels of automation requiring millions of distributed sensors/devices connected into a scalable and smart network. Finally, the automation of critical industrial processes, as well as communication between autonomous vehicles, will require 99.999% reliability and under 1 ms latency as they also become the drivers for the emergence of 5G. Besides traditional sub-6 GHz microwave spectrum, the 5G communication encompasses the novel millimeter-wave bands to mitigate spectrum scarcity and provide large bandwidth of up to several GHz. However, there are challenges to be overcome with the millimeter-wave band. The band suffers from higher pathloss, more atmospheric attenuation, and higher diffraction losses than microwave signals. Because the millimeter-wave band has such a small wavelength (< 1 cm), it is now feasible to implement compact antenna arrays. This enables the use of beamforming and multi-input and multi-output techniques. In this thesis, advanced ray tracing methodology is developed and utilized to simulate the propagation mechanisms and their effect on the system-level metrics. The main novelty of this work is in the introduction of typical millimeter-wave 5G technologies into channel modelling and propagation specifics into the system-level simulation, as well as the adaptation of the ray tracing methods to support extensive simulations with multiple antennas

Evaluation of mmWave 5G Performance by Advanced Ray Tracing Techniques

Technological progress leads to the emergence of new concepts, which can change people’s everyday lives and accelerate the transformation of many industries. Among the more recent of these revolutionary concepts are big data analysis, artificial intelligence, augmented/virtual reality, quantum computing, and autonomous vehicles. However, this list would be incomplete without referring to fifth-generation (5G) technology, which is driven by several trends. First, the exponential growth of the worldwide monthly smartphone traffic up to 50 petabytes during the next three years will require the development of mobile networks supporting high datasharing capabilities, excellent spectral efficiency, and gigabits per second of throughput. Another trend is Industry 4.0/5.0 (also called the smart factory), which refers to advanced levels of automation requiring millions of distributed sensors/devices connected into a scalable and smart network. Finally, the automation of critical industrial processes, as well as communication between autonomous vehicles, will require 99.999% reliability and under 1 ms latency as they also become the drivers for the emergence of 5G. Besides traditional sub-6 GHz microwave spectrum, the 5G communication encompasses the novel millimeter-wave bands to mitigate spectrum scarcity and provide large bandwidth of up to several GHz. However, there are challenges to be overcome with the millimeter-wave band. The band suffers from higher pathloss, more atmospheric attenuation, and higher diffraction losses than microwave signals. Because the millimeter-wave band has such a small wavelength (< 1 cm), it is now feasible to implement compact antenna arrays. This enables the use of beamforming and multi-input and multi-output techniques. In this thesis, advanced ray tracing methodology is developed and utilized to simulate the propagation mechanisms and their effect on the system-level metrics. The main novelty of this work is in the introduction of typical millimeter-wave 5G technologies into channel modelling and propagation specifics into the system-level simulation, as well as the adaptation of the ray tracing methods to support extensive simulations with multiple antennas

Characterization of mmWave Channel Properties at 28 and 60 GHz in Factory Automation Deployments

Future cellular systems are expected to revolutionize today's industrial ecosystem by satisfying the stringent requirements of ultra-high reliability and extremely low latency. Along these lines, the core technology to support the next-generation factory automation deployments is the use of millimeter-wave (mmWave) communication that operates at extremely high frequencies (i.e., from 10 to 100 GHz). However, characterizing the radio propagation behavior in realistic factory environments is challenging due to shorter mmWave wavelengths, which make channel properties be sensitive to the actual topology and size of the surrounding objects. For these reasons, this paper studies the important mmWave channel properties for two distinct types of factories, namely, light industry and heavy industry. These represent the extreme cases of factory classification based on the level of technology, the density and the size of the equipment, and the goods produced. Accordingly, we assess the candidate mmWave frequencies of 28 and 60 GHz for licensed-and unlicensed-band communication, respectively. After analyzing the signal propagation (e.g., in terms of path loss) and the line-of-sight (LoS) probability, our understanding is that in a factory automation environment the presence of metallic equipment and various objects produces many dissimilarities in the mmWave channel properties, thus making them difficult to describe with conventional empirical or stochastic models. Our findings suggest that the deployment of the practical mmWave systems in indoor industrial environments should not therefore rely on past propagation studies available in the literature blindly but might take into account more accurate and reliable evaluation of the environment that is possible with ray-based simulations.acceptedVersionPeer reviewe

Near-ground propagation in automotive radar and communication obstructed deployments : Measurements and modelling

Wireless communication and radars will play a crucial role for autonomous vehicles in the nearest future. However, the blockage caused by surrounding cars can degrade communication performance, while automotive radars are never aimed to operate in such conditions. Therefore, in this paper, the authors propose the concept of near-ground propagation, reducing the blockage effect in the road traffic conditions. Specifically, the radio waves may freely propagate under the blocking car's bottom if the antennas are placed as low as possible to the road. Based on the measured and modelled results presented in the paper, it may be claimed that near-ground communication and radar sensing are feasible and may combat even heavily obstructed cases. Nevertheless, some challenges associated with antenna locations were encountered. For example, it was discovered that antenna height at 0.5 m acts less effectively against blockage than at 0.3 m. Next, the 27 dB excess loss at the 0.5 m antenna height in the radar deployment is larger than 17 dB at 0.3 m. In its turn, the higher ground clearance of the blocking vehicle positively affects the near-ground performance. Additionally, the signal propagation at the grazing angle crucially reduces the relevant losses.publishedVersionPeer reviewe

Deep Learning Based Localization and HO Optimization in 5G NR Networks

In the emerging 5G radio networks, beamforming-capable nodes are able to densely cover narrow areas with a high-quality signal. Such systems require high-level handover management system to proactively react to upcoming changes in signal quality, while restricting common issues such as ping-ponging or fast-shadowing of the signal. The utilization of deep learning in such a system allows for dynamic optimization of the system policies, based directly on the past behavior of the users and their channel responses. Our approach on handover optimization is purely non-deterministic, proving the idea that a self-learning network is able to efficiently manage user mobility in dense network scenario. The proposed network consists of feature extractors and dense layers. The model is trained in two stages, first serves as an initial weight setting in supervised fashion based on 3GPP model. The second stage is an optimization problem to reduce the number of unnecessary handovers while sustaining a high-quality connection. The model is also trained to predict the user location information as the second output. The presented results show that the number of handovers can be significantly reduced without decreasing the throughput of the system. The predicted location of the user has meter-level accuracy.acceptedVersionPeer reviewe

Technologies for Efficient Amateur Drone Detection in 5G Millimeter-Wave Cellular Infrastructure

Unmanned aerial vehicles, also called drones, are recently gaining increased research attention across various fields due to their flexibility and application potential. The steady increase in the number of amateur drones demands more stringent regulations on their allowed route, mass, and load. However, these regulations may be violated accidentally or deliberately. In these cases, spying with drones, transfer of dangerous payloads, or losing reliable drone control can represent a new hazard for people, governments, and business sector. The technologies to detect, track, and disarm possible aerial threats are therefore in prompt demand. To this end, ubiquitous cellular networks, and especially 5G infrastructures based on the use of millimeter-wave radio modules, may be efficiently leveraged to offer the much needed drone detection capabilities. In this work, we propose to exploit 5G millimeter-wave deployments to detect violating amateur drones. We argue that the prospective 5G infrastructure may provide all the necessary technology elements to support efficient detection of small-sized drones. We therefore outline a novel technology and system design perspective, including such considerations as the density of base stations, their directional antennas, and the available bandwidth, among others, as well as characterize their impact with our ray-based modeling methods.acceptedVersionPeer reviewe

Radar Scheme With Raised Reflector for NLOS Vehicle Detection

The employment of passive reflectors enables the millimeter-wave automotive radars to detect an approaching vehicle in non-line-of-sight conditions. In this paper, the installation of such reflectors above the sidewalk at an intersection is proposed and studied, avoiding pedestrians' blockage and road dust effect at ground level. Through the analysis of the backscattering power, it is shown that the suggested scheme may detect an approaching vehicle in the blind zone at distances of 30,&#x0142;dots,50 m to the intersection point. Additionally, the analysis shows that efficient operation is highly dependent on the spatial orientation and size of the reflector. Even a few degrees rotation may change the detecting range by several meters. In turn, the larger area of the reflector may cover longer detecting distances, improving the radar scheme's overall performance. It is also shown that further performance enhancement can be achieved by employing a C-type radar, contributing an extra 5 dB to the backscattering power relative to an A-type radar. However, despite these improvements, the strongest scattering centre of the detectable vehicle is systematically identified to the bumper zone.publishedVersionPeer reviewe

Deep learning-based cell-level and beam-level mobility management system

The deployment with beamforming-capable base stations in 5G New Radio (NR) requires an efficient mobility management system to reliably operate with minimum effort and interruption. In this work, we propose two artificial neural network models to optimize the cell-level and beam-level mobility management. Both models consist of convolutional, as well as dense, layer blocks. Based on current and past received power measurements, as well as positioning information, they choose the optimum serving cell and serving beam, respectively. The obtained results show that the proposed cell-level mobility model is able to sustain a strong serving cell and reduce the number of handovers by up to 94.4% compared to the benchmark solution when the uncertainty (representing shadowing, interference, etc.) is introduced to the received signal strength measurements. The proposed beam-level mobility management model is able to proactively choose and sustain the strongest serving beam, even when high uncertainty is introduced to the measurements.publishedVersionPeer reviewe

Millimeter-Wave Channel Measurements at 28 GHz in Digital Fabrication Facilities

The unprecedented amount of bandwidth available at the millimeter-wave band brings new opportunities for the next-generation factory automation. By supporting these frequencies, the communication technologies may significantly improve the safety and efficiency of manufacturing processes. This paper presents channel measurement results at 28 GHz in a factory environment. The primary channel properties such as path loss, delay, and angular spread are evaluated. Additionally, the distribution of the cross-polarization ratio is shown.acceptedVersionPeer reviewe

Millimeter-Wave Automotive Radar Scheme With Passive Reflector for Blind Corner Conditions

One of the primary functions of millimeter-waveautomotive radar is collision avoidance. This application istypically realized in line-of-sight conditions. However, it does notperform well in situations, when another car suddenly come intoview around the corner of a building. Hence, this paper proposesa radar scheme with a reflector, enabling the detection of anoncoming car in blind corner conditions. First, our ray tracingmodelling results demonstrate the difficulties of straightforwardnon-line-of-sight radar application in such a scenario. Then thepaper considers the installation of a planar reflector, which shouldsolve the issue. This is verified with real-world measurements.The results also indicate that the detection performance issensitive to the around-the-corner car position and orientationof the reflector. Specifically, +/-10 degree deviation varies thesignal-to-noise ratio of more than 20 dB. Thus, the location anddirection of the reflector should be adopted individually for theparticular deployment.acceptedVersionPeer reviewe