Radio network management in cognitive LTE-Femtocell Systems

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London.There is a strong uptake of femtocell deployment as small cell application platforms in the upcoming LTE networks. In such two-tier networks of LTEfemtocell base stations, a large portion of the assigned spectrum is used sporadically leading to underutilisation of valuable frequency resources. Novel spectrum access techniques are necessary to solve these current spectrum inefficiency problems. Therefore, spectrum management solutions should have the features to improve spectrum access in both temporal and spatial manner. Cognitive Radio (CR) with the Dynamic Spectrum Access (DSA) is considered to be the key technology in this research in order to increase the spectrum efficiency. This is an effective solution to allow a group of Secondary Users (SUs) to share the radio spectrum initially allocated to the Primary User (PUs) at no interference. The core aim of this thesis is to develop new cognitive LTE-femtocell systems that offer a 4G vision, to facilitate the radio network management in order to increase the network capacity and further improve spectrum access probabilities. In this thesis, a new spectrum management model for cognitive radio networks is considered to enable a seamless integration of multi-access technology with existing networks. This involves the design of efficient resource allocation algorithms that are able to respond to the rapid changes in the dynamic wireless environment and primary users activities. Throughout this thesis a variety of network upgraded functions are developed using application simulation scenarios. Therefore, the proposed algorithms, mechanisms, methods, and system models are not restricted in the considered networks, but rather have a wider applicability to be used in other technologies. This thesis mainly investigates three aspects of research issues relating to the efficient management of cognitive networks: First, novel spectrum resource management modules are proposed to maximise the spectrum access by rapidly detecting the available transmission opportunities. Secondly, a developed pilot power controlling algorithm is introduced to minimise the power consumption by considering mobile position and application requirements. Also, there is investigation on the impact of deploying different numbers of femtocell base stations in LTE domain to identify the optimum cell size for future networks. Finally, a novel call admission control mechanism for mobility management is proposed to support seamless handover between LTE and femtocell domains. This is performed by assigning high speed mobile users to the LTE system to avoid unnecessary handovers. The proposed solutions were examined by simulation and numerical analysis to show the strength of cognitive femtocell deployment for the required applications. The results show that the new system design based on cognitive radio configuration enable an efficient resource management in terms of spectrum allocation, adaptive pilot power control, and mobile handover. The proposed framework and algorithms offer a novel spectrum management for self organised LTE-femtocell architecture. Eventually, this research shows that certain architectures fulfilling spectrum management requirements are implementable in practice and display good performance in dynamic wireless environments which recommends the consideration of CR systems in LTE and femtocell networks

Airport connectivity optimization for 5G ultra-dense networks

The rapid increase of air traffic demand and complexity of radio access network motivate developing scalable wireless communications by adopting system intelligence. The lack of adaptive reconfiguration in radio transmission systems may cause dramatic impacts on the traffic management concerning congestion and demand-capacity imbalances driving the industry to jointly access licensed and unlicensed bands for improved airport connectivity. Therefore, intelligent system is embedded into fifth generation (5G) ultra-dense networks (UDNs) to provision dense and irregular deployments that maintain extended coverage and also to improve the energy-efficiency for the entire airport network providing high speed services. To define the technical aspects of this solution, this paper addresses new intelligent technique that configures the coverage and capacity factors of radio access network considering the changes in air traffic demands. This technique is analysed through mathematical models that employ power consumption constraints to support dynamic traffic control requirements to improve the overall network capacity. The presented problem is formulated and exactly solved for medium or large airport air transportation network. The power optimization problem is solved using linear programming with careful consideration to latency and energy efficiency factors. Specifically, an intelligent pilot power method is adopted to maintain the connectivity throughout multi-interface technologies by assuming minimum power requirements. Numerical and system-level analysis are conducted to validate the performance of the proposed schemes for both licenced macrocell Long-Term Evolution (LTE) and unlicensed wireless fidelity (WiFi) topologies. Finally, the insights of problem modelling with intelligent techniques provide significant advantages at reasonable complexity and brings the great opportunity to improve the airport network capacit

Energy efficiency using cloud management of LTE networks employing fronthaul and virtualized baseband processing pool

The cloud radio access network (C-RAN) emerges as one of the future solutions to handle the ever-growing data traffic, which is beyond the physical resources of current mobile networks. The C-RAN decouples the traffic management operations from the radio access technologies, leading to a new combination of a virtualized network core and a fronthaul architecture. This new resource coordination provides the necessary network control to manage dense Long-Term Evolution (LTE) networks overlaid with femtocells. However, the energy expenditure poses a major challenge for a typical C-RAN that consists of extended virtualized processing units and dense fronthaul data interfaces. In response to the power efficiency requirements and dynamic changes in traffic, this paper proposes C-RAN solutions and algorithms that compute the optimal backup topology and network mapping solution while denying interfacing requests from low-flow or inactive femtocells. A graph-coloring scheme is developed to label new formulated fronthaul clusters of femtocells using power as the performance metric. Additional power savings are obtained through efficient allocations of the virtualized baseband units (BBUs) subject to the arrival rate of active fronthaul interfacing requests. Moreover, the proposed solutions are used to reduce power consumption for virtualized LTE networks operating in the Wi-Fi spectrum band. The virtualized network core use the traffic load variations to determine those femtocells who are unable to transmit to switch them off for additional power savings. The simulation results demonstrate an efficient performance of the given solutions in large-scale network models

Power interchange analysis for reliable vehicle-to-grid connectivity

Due to the progressively growing energy demand, electricl vehicles (EVs) are increasingly replacing unfashionable vehicles equipped with internal combustion engines. The new era of modern grid is aiming to unlock the possibility of resource coordination between EVs and power grid. The goal of including vehicle-to-grid (V2G) technology is to enable shared access to power resources. To define the initiative, this article investigates the bidirectional power flow between EVs and the main grid. The article provides a new algorithm framework for energy optimization that enables real-time decision making to facilitate charge/discharge processes in grid connected mode. Accordingly, the energy flow optimization, communications for data exchange, and local controller are joined to support system reliability for both power grid and EV owners at parking lot sites. The local controller is the key component that collects the EV data for decision making through real-time communications with EV platforms. The main responsibility of this controller is managing the energy flow during the process of real-time charging without impacting the basic functionalities of both grid and EV systems. Finally, a case study of a modified IEEE 13-node test feeder is proposed to validate the impact of energy flow optimization using V2G technology. This visionary concept provides improvement in grid scalability and reliability to grid operations through accessing EV power storage as a complementary resource of future energy systems

A framework of network connectivity management in multi-clouds infrastructure

The network function virtualization (NFV) transformation is gaining an incredible momentum from mobile operators as one of the significant solutions to improve the resource allocation and system scalability in fifth-generation (5G) networks. However, the ultra-dense deployments in 5G create high volumes of traffic that pushes the physical and virtual resources of cloud-based networks to edge limits. Consider a distributed cloud, replacing the core network with virtual entities in the form of virtual network functions (VNFs) still requires efficient integration with various underlying hardware technologies. Therefore, orchestrating the distribution of load between cloud geo-datacenters starts by instantiating a virtual and physical network typologies that connect involved front haul with relevant VNFs. In this article, we provide a framework to manage calls within 5G network clusters for efficient utilization of computational resources and also to prevent unnecessary signaling. We also propose a new scheme to instantiate virtual tunnels for call forwarding between network clusters leading to new logic networks that combine geo-datacenters and fronthaul. To facilitate service chaining in cloud, we propose a new enhanced management and orchestration (E-MANO) architecture that brings network traffic policies from the application layer tothe fronthaul for instant monitoring of available resources. We provide analysis and testbed results in support of our proposals. the fronthaul for instant monitoring of available resources. We provide analysis and testbed results in support of our proposals

UTM regulatory concerns with machine learning and artificial intelligence

Artificial intelligence (AI) and machine learning will have a significant impact on the application of drones and the integration of universal/unmanned traffic management (UTM) that relate to unmanned operations in urban environments at very low-level airspace. Artificial intelligence will necessitate high levels of automation and act as an enabler with respect to the integration of unmanned and manned aviation and will ultimately enable safe operations with respect to high numbers of drones utilising the same airspace, and more specifically with respect to detect and avoid capability. AI is going to be heavily developed and utilised by organisations that certify as U-space service providers (USSP’s) when providing a service to Unmanned Aerial Systems (UAS) Operators. The equipment utilised by UAS Operators will to some extent already benefit from AI, but the level of automation is currently constrained by regulation. A legal framework must exist, as AI will not only have a significant impact upon existing laws but will ensure a framework that facilitates safety and the fundamental rights of citizens and businesses with respect to AI. The EU has published a proposed law, namely the Artificial Intelligence Act as permitted under Article 114 of the Treaty on the Functioning of the European Union (TFEU)

Unmanned aerial vehicle positioning using 5G new radio technology in urban environment

Unmanned aerial vehicles (UAVs) are becoming increasingly popular for various applications, including surveillance, monitoring, mapping, delivery, and inspection. However, their positioning capabilities in urban environments can be limited due to challenges such as Non-Line-of-Sight (NLOS) propagation, multi-path interference, and signal blockage caused by tall buildings, trees, and other obstacles, which can affect their positioning capabilities. The purpose of this paper is to provide a novel approach for UAV’s positioning based on Observed Time Difference of Arrival (OTDOA), combining 5G (NR) technology and an inertial measurement unit (IMU) to improve UAV positioning in urban environments. Integrating these technologies can improve UAV positioning and control systems by offering rapid, low-latency communication, a thorough and precise comprehension of the UAV’s surroundings and its own condition, and more accurate assessments of the UAV’s location, speed, and orientation. Simulation model shows the data from these sensors is then fused using an Extended Kalman Filter (EKF) to estimate the UAV’s position and orientation. The study shows that the proposed system delivers accurate and reliable UAV positioning in these environments, outperforming traditional methods

Analysis of synchronization in distributed avionics systems based on time-triggered ethernet

Significant developments are made in unmanned aerial vehicles (UAVs) and in avionics, where messages sent in the network with critical Time play a vital role. Several studies in Time-Triggered Ethernet have been carried out, but these studies. Still, these improving QoS such as latency in end-to-end delays of an internally synchronized TTE network. However, no one monitors integrated modular avionics. We proposed a framework that enables TTE to be externally synchronized from a GNSS to overcome this problem. We have incorporated our proposed Algorithm in the TTE protocol based on specific parameters and multiple existing algorithms. The proposed Algorithm gives us the ability to control and synchronize the TTE network. Also, we have a developed scenario for analyzing the performance of externally synchronized end-to-end latency of TT messages in a TTE network. We simulated scenarios in our framework and analyzed QoS but, more specifically, the latency that affects the performance of time-triggered messages in externally synchronized TTE networks. The result shows that our proposed framework outperforms existing approaches

Handover prediction for aircraft dual connectivity using model predictive control

Providing connectivity to aircraft such as flying taxis is a significant challenge for tomorrow’s aviation communication systems. One major problem is to provide ground to air (G2A) connectivity, especially in the airport, rural and sub-rural areas where the number of radio ground stations is not adequate to support the data link resulting in frequent interruption. Hence, effective handover decision-making is necessary to provide uninterrupted services to aircraft while moving from one domain to another. However, the existing handover decision is not efficient enough to solve the aircraft connectivity in such airspace. To overcome this problem, a prediction based optimal solution to handover decision making (handover prediction) would be appropriate to provide seamless dual connectivity to aircraft. In this paper, the handover prediction problem is formulated as a constrained optimization problem in the framework of the model for predictive control (MPC). The cost function and the constraints are derived in terms of dual connectivity variables over the prediction horizon. This problem is solved using a two-dimensional genetic algorithm (2D-GA) to obtain the predictive optimal handover solution. Simulation results show that the proposed dual connectivity handover can significantly improve the handover success probability. Finally, our results show that network densification and predictive control model have improved aircraft performance

Intelligent embedded systems platform for vehicular cyber-physical systems

Intelligent vehicular cyber-physical systems (ICPSs) increase the reliability, efficiency and adaptability of urban mobility systems. Notably, ICPSs enable autonomous transportation in smart cities, exemplified by the emerging fields of self-driving cars and advanced air mobility. Nonetheless, the deployment of ICPSs raises legitimate concerns surrounding safety assurance, cybersecurity threats, communication reliability, and data management. Addressing these issues often necessitates specialised platforms to cater to the heterogeneity and complexity of ICPSs. To address this challenge, this paper presents a comprehensive CPS to explore, develop and test ICPSs and intelligent vehicular algorithms. A customisable embedded system is realised using a field programmable gate array, which is connected to a supervisory computer to enable networked operations and support advanced multi-agent algorithms. The platform remains compatible with multiple vehicular sensors, communication protocols and human–machine interfaces, essential for a vehicle to perceive its surroundings, communicate with collaborative systems, and interact with its occupants. The proposed CPS thereby offers a practical resource to advance ICPS development, comprehension, and experimentation in both educational and research settings. By bridging the gap between theory and practice, this tool empowers users to overcome the complexities of ICPSs and contribute to the emerging fields of autonomous transportation and intelligent vehicular systems