Modelling 3D blockage effects for millimetre-wave communication systems

The millimetre wave (mmWave) band, which has a frequency range of 30-300 GHz, can provide the desired requirements for future communication systems, such as wide bandwidth and high data-rate with very low latency. However, these advantages entail several consequences and challenges: compared with the microwave band, below 6 GHz, the mmWave band not only suffers from increased path loss but also higher sensitivity to blockage effects due to very short wavelengths. Considering the mmWave band, a human blockage, for example, could severely affect the transmitted signal by causing attenuation of 20 dB or more. With motion, the attenuation problem becomes even more serious. The rapid changes of dynamic blockages surrounding a moving transceiver can cause a significant and sudden impact on channel attenuation, which affects the overall quality of service for mmWave systems. The main scope of this thesis is to develop new mathematical models that accurately capture the dynamics of blockers affecting a moving transceiver in order to compute the resulting channel attenuation accurately. The first Markov chain model studied in this work follows a simple approach by assigning a fixed-attenuation value to each blocker and using a geometric model to generate the transition probability matrices. The transition probabilities are calculated both analytically and via a geometric simulation, where the results are found to match well. The proposed model successfully captures the dynamics of the channel caused by blockers surrounding a moving transceiver. The model works well for stationary scenarios, and the proposed technique of switching between several Markov chains makes the model applicable to a non-stationary average number of blockers as well. The adaptive sum of Markov chains (sum of MC) is another proposed model, which can model the dynamics of blockage effects more accurately than the simpler Markov Chain model. It is adaptive to non-stationary scenarios in any given environment, and it efficiently captures the dynamics of blockages arising from a moving transceiver. The sum of Markov chains model can integrate any desired attenuation function, including the third-generation partnership project (3GPP) blockage model and any lab measurement attenuation profile. The sum of MC model could be a very useful tool for communication engineers, allowing them to perform an initial mmWave coverage analysis for a given environment in the presence of time-varying blockage effects. Unlike human blockage, which has been widely studied in the literature, the impact of other small objects on signal strength, such as metal road signs, is not so well understood. This thesis has carried out a measurement campaign for these small blockers, which induce measured loss in the range of 15- 30 dB, depending on the type and size of the blocker. The thesis also compares those results with existing simulation blockage models for these small objects. These blockage models include the 3GPP model, the multiple knife-edge (MKE) model, and the mmMAGIC model, where the latter two models show a better fit to the measured attenuation of relatively small blockers than the 3GPP model. Finally, the thesis evaluates the impact of blockers on the overall performance of mmWave multiple-input multiple-output (MIMO) wireless systems, where a ray-tracing tool is used to establish all possible propagation paths for a moving transceiver in an outdoor scenario. The performance impact of the measured attenuation profiles for road signs are evaluated for an outdoor scenario using the sum of MC model

Impact of blockage and mobility on collaborative sensing and millimeter wave based communication

This dissertation considers the impact of blockage and mobility on collaborative sensing and millimeter wave (mmWave) based communication networks. We first study the character of interference and MAC performance in dense indoor mmWave wearable networks. Using simple stochastic geometric models for propagation in mmWave bands, we quantify the number of strong interferers as seen by a typical receiver and show that it is limited due to blockage. We propose a model to evaluate the performance of current MAC designs using clustering and hierarchical scheduling. Our results show that the MAC overheads are scalable, i.e., the performance optimal cluster size does not grow with user density in dense scenarios. Furthermore, we show that at high densities the per user throughput is eventually constant. Next we consider the impact of blockage mobility on MAC overheads and performance in such networks. We propose a stochastic geometric model to capture the temporal dynamics of strong interfering channels resulting from blocking in networks comprising both fixed and mobile blockages. Based on our analysis, we derive the rate of change in channels' states, i.e., Line-of-Sight (LOS) and Non-LOS (NLOS), and estimate the signaling overheads resulting from blockage mobility. We argue that while the overheads to track the interference environment may in fact be limited, MAC protocols will most likely be better off not coordinating with distant and/or mobile nodes. We then move on to another area where obstructions have a major impact, i.e., collaborative sensing for automated driving applications. Both the sensing and communication for collaborative sensing may be subject to obstructions (blockages) in such a collaborative setting. We introduce new models for vehicular collaborative sensing and networking under obstructions and evaluate how "performance" scales. In particular, we quantify the coverage and reliability gains obtained by collaborative sensing as a function of the penetration of collaborative vehicles. We further evaluate the associated communication loads in terms of vehicle-to-vehicle (V2V) and/or vehicle-to-infrastructure (V2I) capacity requirements and how these depend on penetration. Sensing by a single vehicle can be greatly limited by obstructing neighboring vehicles and objects, while collaborative sensing is shown to greatly improve sensing performance, e.g., improves coverage from 20% to 80% with a 20% penetration. Furthermore, the volume of sensor data a vehicle generates and needs to share for collaborative sensing does not necessarily increase with the density of objects. In scenarios with limited penetration and enhanced reliability requirements, infrastructure can be used to sense the environment and relay data. Once penetration is high enough, vehicular collaborative sensing provides good coverage and V2V connectivity. Data traffic can be effectively 'offloaded' to V2V network, making V2I resources available to support other services. Finally we present a more detailed evaluation of the performance of collaborative sensing assisted by sensing capable infrastructure, including Road Side Units (RSUs) and sensors on cellular infrastructure. We compare the performance of different infrastructure and deployment schemes in terms of collaborative sensing coverage. Unless deployed along roads, cellular based sensors off the roads are more obstructed and RSUs deployed at intersections and at even spacings appear more desirable. Simulation results show that RSUs see fewer environmental obstructions when placed higher than vehicles and can benefit from temporal diversity in sensing. Although RSUs have good sensing coverage, in order to communicate with the relevant vehicle, they will require relatively high communication range, rate and reliability. Even if RSUs provide complete coverage of the roads, to increase reliability of sensing, e.g., redundancy in sensing, collaboration amongst sensing capable vehicles may still be desirable.Electrical and Computer Engineerin

Statistical millimeter wave channel modelling for 5G and beyond

Millimetre wave (mmWave) wireless communication is one of the most promising technologies for the fifth generation (5G) wireless communication networks and beyond. The very broad bandwidth and directional propagation are the two features of mmWave channels. In order to develop the channel models properly reflecting the characteristics of mmWave channels, the in-depth studies of mmWave channels addressing those two features are required. In this thesis, three mmWave channel models and one beam alignment scheme are proposed related to those two features. First, for studying the very broad bandwidth feature of mmWave channels, we introduce an averaged power delay profile (APDP) method to estimate the frequency stationarity regions (FSRs) of channels. The frequency non-stationary (FnS) properties of channels are found in the data analysis. A FnS model is proposed to model the FnS channels in both the sub-6 GHz and mmWave frequency bands and cluster evolution in the frequency domain is utilised in the implementation of FnS model. Second, for studying the directional propagation feature of mmWave channels, we develop an angular APDP (A-APDP) method to study the planar angular stationarity regions (ASRs) of directional channels (DCs). Three typical directional channel impulse responses (D-CIRs) are found in the data analysis and light-of-sight (LOS), non-LOS (NLOS), and outage classes are used to classify those DCs. A modified Saleh-Valenzuela (SV) model is proposed to model the DCs. The angular domain cluster evolution is utilised to ensure the consistency of DCs. Third, we further extend the A-APDP method to study the spherical-ASRs of DCs. We model the directional mmWave channels by three-state Markov chain that consists of LOS, NLOS, and outage states and we use stationary model, non-stationary model, and “null” to describe the channels in each Markov state according to the estimated ASRs. Then, we propose to use joint channel models to simulate the instantaneous directional mmWave channels based on the limiting distribution of Markov chain. Finally, the directional propagated mmWave channels when the Tx and Rx in motion is addressed. A double Gaussian beams (DGBs) scheme for mobile-to-mobile (M2M) mmWave communications is proposed. The connection ratios of directional mmWave channels in each Markov state are studied

Beam alignment for millimeter wave vehicular communications

Millimeter wave (mmWave) has the potential to provide vehicles with high data rate communications that will enable a whole new range of applications. Its use, however, is not straightforward due to its challenging propagation characteristics. One approach to overcome the propagation challenge is the use of directional beams, but it requires a proper alignment and presents a challenging engineering problem, especially under the high vehicular mobility. In this dissertation, fast and efficient beam alignment solutions suitable for vehicular applications are developed. To better quantify the problem, first the impact of directional beams on the temporal variation of the channels is investigated theoretically. The proposed model includes both the Doppler effect and the pointing error due to mobility. The channel coherence time is derived, and a new concept called the beam coherence time is proposed for capturing the overhead of mmWave beam alignment. Next, an efficient learning-based beam alignment framework is proposed. The core of this framework is the beam pair selection methods that use side information (position in this case) and past beam measurements to identify promising beam directions and eliminate unnecessary beam training. Three offline learning methods for beam pair selection are proposed: two statistics-based and one machine learning-based methods. The two statistical learning methods consist of a heuristic and an optimal selection that minimizes the misalignment probability. The third one uses a learning-to-rank approach from the recommender system literature. The proposed approach shows an order of magnitude lower overhead than existing standard (IEEE 802.11ad) enabling it to support large arrays at high speed. Finally, an online version of the optimal statistical learning method is developed. The solution is based on the upper confidence bound algorithm with a newly introduced risk-aware feature that helps avoid severe misalignment during the learning. Along with the online beam pair selection, an online beam pair refinement is also proposed for learning to adapt the codebook to the environment to further maximize the beamforming gain. The combined solution shows a fast learning behavior that can quickly achieve positive gain over the exhaustive search on the original (and unrefined) codebook. The results show that side information can help reduce mmWave link configuration overhead.Electrical and Computer Engineerin

A Tutorial on Mathematical Modeling of 5G/6G Millimeter Wave and Terahertz Cellular Systems

Millimeter wave (mmWave) and terahertz (THz) radio access technologies (RAT) are expected to become a critical part of the future cellular ecosystem providing an abundant amount of bandwidth in areas with high traffic demands. However, extremely directional antenna radiation patterns that need to be utilized at both transmit and receive sides of a link to overcome severe path losses, dynamic blockage of propagation paths by large static and small dynamic objects, macro-and micromobility of user equipment (UE) makes provisioning of reliable service over THz/mmWave RATs an extremely complex task. This challenge is further complicated by the type of applications envisioned for these systems inherently requiring guaranteed bitrates at the air interface. This tutorial aims to introduce a versatile mathematical methodology for assessing performance reliability improvement algorithms for mmWave and THz systems. Our methodology accounts for both radio interface specifics as well as service process of sessions at mmWave/THz base stations (BS) and is capable of evaluating the performance of systems with multiconnectivity operation, resource reservation mechanisms, priorities between multiple traffic types having different service requirements. The framework is logically separated into two parts: (i) parameterization part that abstracts the specifics of deployment and radio mechanisms, and (ii) queuing part, accounting for details of the service process at mmWave/THz BSs. The modular decoupled structure of the framework allows for further extensions to advanced service mechanisms in prospective mmWave/THz cellular deployments while keeping the complexity manageable and thus making it attractive for system analysts.publishedVersionPeer reviewe

A Tutorial on Mathematical Modeling of 5G/6G Millimeter Wave and Terahertz Cellular Systems

Millimeter wave (mmWave) and terahertz (THz) radio access technologies (RAT) are expected to become a critical part of the future cellular ecosystem providing an abundant amount of bandwidth in areas with high traffic demands. However, extremely directional antenna radiation patterns that need to be utilized at both transmit and receive sides of a link to overcome severe path losses, dynamic blockage of propagation paths by large static and small dynamic objects, macro-and micromobility of user equipment (UE) makes provisioning of reliable service over THz/mmWave RATs an extremely complex task. This challenge is further complicated by the type of applications envisioned for these systems inherently requiring guaranteed bitrates at the air interface. This tutorial aims to introduce a versatile mathematical methodology for assessing performance reliability improvement algorithms for mmWave and THz systems. Our methodology accounts for both radio interface specifics as well as service process of sessions at mmWave/THz base stations (BS) and is capable of evaluating the performance of systems with multiconnectivity operation, resource reservation mechanisms, priorities between multiple traffic types having different service requirements. The framework is logically separated into two parts: (i) parameterization part that abstracts the specifics of deployment and radio mechanisms, and (ii) queuing part, accounting for details of the service process at mmWave/THz BSs. The modular decoupled structure of the framework allows for further extensions to advanced service mechanisms in prospective mmWave/THz cellular deployments while keeping the complexity manageable and thus making it attractive for system analysts.publishedVersionPeer reviewe

Navigation coopérative de véhicules autonomes basée sur la communication V2X dans un réseau de 5ème génération

In today’s world, road transport is essential to our daily routines and business activities. However, the exponential growth in the number of vehicles has led to problems such as traffic congestion and road accidents. Vehicular communication presents an innovative solution, envisaging a future where vehicles communicate with each other, the road infrastructure, and even the road itself, sharing real-time data to optimize traffic flow and enhance safety. This thesis focuses on 5G and Beyond 5G (B5G) technologies, which promise to revolutionize Vehicle-to-Everything (V2X) communication. With the emergence of millimeter-wave (mmWave) communication, high-speed, low-latency data transmission is essential for vehicular networks. However, mmWave communication faces problems with signal attenuation and interference. Our research focuses on solving these problems using a deep learning-based approach. Three significant contributions are proposed. First, we introduce a classical optimization technique, the simulated annealing algorithm, to improve beam alignment in 5G vehicular networks. This reduces latency and improves data transmission between millimeter-wave base stations and vehicles. Our second contribution is a new approach involving a hybrid deep-learning model that predicts optimal beam angles. Combining a 1D CNN and a BiLSTM improves th accuracy of the prediction and reduces errors. This approach eliminates time-consuming computations and iterations critical to the success of B5G vehicular networks. The third contribution introduces a BiLSTM-based model to select the optimal beam pair angles at the mmWave base station (mmBS) and the moving vehicle side. This approach improves the reliability of data transmission while minimizing the error probabilities and overheads during beam search. This research contributes to advancing vehicular communications, offering innovative solutions for 5G and B5G networks. We aim to enhance the efficiency, reduce the latency, and improve the reliability of communications for connected vehicles. This thesis explores beam alignment through classical and deep learning techniques and presents solutions for the challenges of millimeter-wave vehicular networks. Our research provides the foundation for the next generation of vehicular communication and its vital role in making road transport safer and more efficient