Browsing by Author "Al-Rubaye, Saba"
Now showing 1 - 20 of 47
Results Per Page
Sort Options
Item Open Access 5G aviation networks using novel AI approach for DDoS detection(IEEE, 2023-07-17) Whitworth, Huw; Al-Rubaye, Saba; Tsourdos, Antonios; Jiggins, JuliaThe advent of Fifth Generation (5G) technology has ushered in a new era of advancements in the aviation sector. However, the introduction of smart infrastructure has significantly altered the threat landscape at airports, leading to an increased vulnerability due to the proliferation of endpoints. Consequently, there is an urgent requirement for an automated detection system capable of promptly identifying and thwarting network intrusions. This research paper proposes a deep learning methodology that merges a Convolutional Neural Network (CNN) with a Gated Recurrent Unit (GRU) to effectively detect various types of cyber threats using tabular-based image data. To transform time series features into 2D texture images, Gramian Angular Fields (GAFs) are utilized. These images are then stacked to form an N-channel image, which is fed into the CNN-GRU architecture for sequence analysis and identification of potential threats. The provide solution GAF-CNN-GRU achieved an accuracy of 98.6% on the Cranfield Embedded Systems Attack Dataset. We further achieved Precision, Recall and F1-scores of 97.84%, 91% and 94.3%. To evaluate model robustness we further tested this approach, using a benchmark random selection of input features, on the Canadian Institute for Cyber-Security (CIC) 2019 Distributed Denial-of-service attack (DDoS) Dataset achieving an Accuracy of 89.08%. Following feature optimisation our approach was able to achieve an accuracy of 98.36% with Precision, Recall and F1 scores of 93.09%, 95.45% and 94.56% respectively.Item Open Access Adaptive detection tracking system for autonomous UAV maritime patrolling(IEEE, 2020-08-06) Panico, Alessandro; Zanotti Fragonara, Luca; Al-Rubaye, SabaNowadays, Unmanned Aerial Vehicles (UAVs) are considered reliable systems, suitable for several autonomous applications, especially for target detection and tracking. Although significant developments were achieved in object detection systems over the last decades using the deep learning technique known as Convolutional Neural Networks (CNN), there are still research gaps in this area. In this paper, we present a new object detection-tracking algorithm that can be used on low power consuming processing boards. In particular, we analysed a specific application scenario in which a UAV patrols coastlines and autonomously classifies different kind of marine objects. Current state of the art solutions propose centralised architectures or flying systems with human in the loop, making the whole system poorly efficient and not scalable. On the contrary, applying a Deep Learning detection system that runs on commercial Graphics Processing Units (GPUs) makes UAVs potentially more efficient than humans (especially for dull tasks like coastline patrolling) and the whole system becomes easily scalable because each UAV can fly independently and the Ground Control Station does not represent a bottleneck. To deal with this task, a database consisting of more than 115000 images was created to train and test several CNN architectures. Furthermore, an adaptive detection-tracking algorithm was introduced to make the whole system faster by optimizing the balancing between detecting new objects and tracking existing targets. The proposed solution is based on the measure of the tracking confidence and the frame similarity, by means of the Structural SIMilarity (SSIM) index, computed both globally and locally. Finally, the developed algorithms were tested on a realistic scenario by means of a UAV test-bed.Item Open Access Advanced air mobility operation and infrastructure for sustainable connected eVTOL vehicle(MDPI, 2023-05-16) Al-Rubaye, Saba; Tsourdos, Antonios; Namuduri, KameshAdvanced air mobility (AAM) is an emerging sector in aviation aiming to offer secure, efficient, and eco-friendly transportation utilizing electric vertical takeoff and landing (eVTOL) aircraft. These vehicles are designed for short-haul flights, transporting passengers and cargo between urban centers, suburbs, and remote areas. As the number of flights is expected to rise significantly in congested metropolitan areas, there is a need for a digital ecosystem to support the AAM platform. This ecosystem requires seamless integration of air traffic management systems, ground control systems, and communication networks, enabling effective communication between AAM vehicles and ground systems to ensure safe and efficient operations. Consequently, the aviation industry is seeking to develop a new aerospace framework that promotes shared aerospace practices, ensuring the safety, sustainability, and efficiency of air traffic operations. However, the lack of adequate wireless coverage in congested cities and disconnected rural communities poses challenges for large-scale AAM deployments. In the immediate recovery phase, incorporating AAM with new air-to-ground connectivity presents difficulties such as overwhelming the terrestrial network with data requests, maintaining link reliability, and managing handover occurrences. Furthermore, managing eVTOL traffic in urban areas with congested airspace necessitates high levels of connectivity to support air routing information for eVTOL vehicles. This paper introduces a novel concept addressing future flight challenges and proposes a framework for integrating operations, infrastructure, connectivity, and ecosystems in future air mobility. Specifically, it includes a performance analysis to illustrate the impact of extensive AAM vehicle mobility on ground base station network infrastructure in urban environments. This work aims to pave the way for future air mobility by introducing a new vision for backbone infrastructure that supports safe and sustainable aviation through advanced communication technology.Item Open Access An advanced path planning and UAV relay system: enhancing connectivity in rural environments(MDPI, 2024-03-06) El Debeiki, Mostafa; Al-Rubaye, Saba; Perrusquía, Adolfo; Conrad, Christopher; Flores Campos, Juan AlejandroThe use of unmanned aerial vehicles (UAVs) is increasing in transportation applications due to their high versatility and maneuverability in complex environments. Search and rescue is one of the most challenging applications of UAVs due to the non-homogeneous nature of the environmental and communication landscapes. In particular, mountainous areas pose difficulties due to the loss of connectivity caused by large valleys and the volumes of hazardous weather. In this paper, the connectivity issue in mountainous areas is addressed using a path planning algorithm for UAV relay. The approach is based on two main phases: (1) the detection of areas of interest where the connectivity signal is poor, and (2) an energy-aware and resilient path planning algorithm that maximizes the coverage links. The approach uses a viewshed analysis to identify areas of visibility between the areas of interest and the cell-towers. This allows the construction of a blockage map that prevents the UAV from passing through areas with no coverage, whilst maximizing the coverage area under energy constraints and hazardous weather. The proposed approach is validated under open-access datasets of mountainous zones, and the obtained results confirm the benefits of the proposed approach for communication networks in remote and challenging environments.Item Unknown AI-driven blind signature classification for IoT connectivity: a deep learning approach(IEEE, 2022-01-31) Pan, Jianxiong; Ye, Neng; Yu, Hanxiao; Hong, Tao; Al-Rubaye, Saba; Mumtaz, Shahid; Al-Dulaimi, Anwer; Chih-Lin, I.Non-orthogonal multiple access (NOMA) promises to fulfill the fast-growing connectivities in future Internet of Things (IoT) using abundant multiple-access signatures. While explicitly notifying the utilized NOMA signatures causes large signaling cost, blind signature classification naturally becomes a low-cost option. To accomplish signature classification for NOMA, we study both likelihood- and feature-based methods. A likelihood-based method is firstly proposed and showed to be optimal in the asymptotic limit of the observations, despite high computational complexity. While feature-based classification methods promise low complexity, efficient features are non-trivial to be manually designed. To this end, we resort to artificial intelligence (AI) for deep learning-based automatic feature extraction. Specifically, our proposed deep neural network for signature classification, namely DeepClassifier, establishes on the insights gained from the likelihood-based method, which contains two stages to respectively deal with a single observation and aggregate the classification results of an observation sequence. The first stage utilizes an iterative structure where each layer employs a memory-extended network to explicitly exploit the knowledge of signature pool. The second stage incorporates the straight-through channels within a deep recurrent structure to avoid information loss of previous observations. Experiments show that DeepClassifier approaches the optimal likelihood-based method with a reduction of 90% complexity.Item Unknown AI-enabled interference mitigation for autonomous aerial vehicles in urban 5G networks(MDPI, 2023-10-13) Warrier, Anirudh; Al-Rubaye, Saba; Inalhan, Gokhan; Tsourdos, AntoniosIntegrating autonomous unmanned aerial vehicles (UAVs) with fifth-generation (5G) networks presents a significant challenge due to network interference. UAVs’ high altitude and propagation conditions increase vulnerability to interference from neighbouring 5G base stations (gNBs) in the downlink direction. This paper proposes a novel deep reinforcement learning algorithm, powered by AI, to address interference through power control. By formulating and solving a signal-to-interference-and-noise ratio (SINR) optimization problem using the deep Q-learning (DQL) algorithm, interference is effectively mitigated, and link performance is improved. Performance comparison with existing interference mitigation schemes, such as fixed power allocation (FPA), tabular Q-learning, particle swarm optimization, and game theory demonstrates the superiority of the DQL algorithm, where it outperforms the next best method by 41.66% and converges to an optimal solution faster. It is also observed that, at higher speeds, the UAV sees only a 10.52% decrease in performance, which means the algorithm is able to perform effectively at high speeds. The proposed solution effectively integrates UAVs with 5G networks, mitigates interference, and enhances link performance, offering a significant advancement in this field.Item Open Access Aircraft to operations communication analysis and architecture for the future aviation environment(IEEE, 2021-11-15) Whitworth, Huw; Al-Rubaye, Saba; Tsourdos, Antonios; Jiggins, Julia; Silverthorn, Nigel; Thomas, KarimFifth Generation (5G) systems are envisaged to support a wide range of applications scenarios with varying requirements. 5G architecture includes network slicing abilities which facilitate the partitioning of a single network infrastructure on to multiple logical networks, each tailored to a given use case, providing appropriate isolation and Quality of Service (QoS) characteristics. Radio Access Network (RAN) slicing is key to ensuring appropriate QoS over multiple domains; achieved via the configuration of multiple RAN behaviours over a common pool of radio resources. This Paper proposes a novel solution for efficient resource allocation and assignment among a variety of heterogeneous services, to utilize the resources while ensuring maximum QoS for network services. First, this paper evaluates the effectiveness of different wireless data bearers. Secondly, the paper proposes a novel dynamic resource allocation algorithm for RAN slicing within 5G New Radio (NR) networks utilising cooperative game theory combined with priority-based bargaining. The impact of this work to industry is to provide a new technique for resource allocation that utilizes cooperative bargaining to ensure all network services achieve minimum QoS requirements – while using application priority to reduce data transfer time for key services to facilitate decreased turnaround time at the gate.Item Open Access Airport connectivity optimization for 5G ultra-dense networks(IEEE, 2020-06-08) Al-Rubaye, Saba; Tsourdos, AntoniosThe 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 capacityItem Open Access Analysis of synchronization in distributed avionics systems based on time-triggered ethernet(IEEE, 2021-11-15) Tariq, Nahman; Petrunin, Ivan; Al-Rubaye, SabaSignificant 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.Item Open Access Attack-detection architectural framework based on anomalous patterns of system performance and resource utilization - Part II(IEEE, 2021-06-11) Aloseel, Abdulmohsan; Al-Rubaye, Saba; Zolotas, Argyrios; Shaw, CarlThis paper presents a unique security approach for detecting cyber-attacks against embedded systems (ESs). The proposed approach has been shaped within an architectural framework called anomalous resource consumption detection (ARCD). The approach’s detection mechanism detects cyber-attacks by distinguishing anomalous performance and resource consumption patterns from a pre-determinable reference model. The defense mechanism of this approach acts as an additional layer of protection for ESs. This technique’s effectiveness was previously evaluated statistically, and in this paper, we tested this approach’s efficiency computationally by using the support-vector machine algorithm. The datasets were generated and collected based on a testbed model, where it was run repeatedly under different operation conditions (normal cases (Rs) versus attacked cases). The executed attack scenarios are 1) denial-of-service (DoS); 2) brute force (BF); and 3) remote code execution (RCE), and man-in-the-middle (MITM). A septenary tuple model, which consists of seven determinants that are analyzed based on seven statistical criteria, is the core of the detection mechanism. The prediction accuracy in terms of classifying anomalous patterns compared to normal patterns based on the confusion matrix revealed promising results, proving this approach’s effectiveness, where the final results confirmed very high prediction accuracies in terms of distinguishing anomalous patterns from the typical patterns. Integrating the ARCD concept into an operating system’s functionality could help software developers augment the existing security countermeasures of ESs. Adopting the ARCD approach will pave the way for software engineers to build more secure operating systems in line with the embedded system’s capabilities, without depleting its resources.Item Open Access Blockchain-based secure and intelligent data dissemination framework for UAVs in battlefield applications(IEEE, 2023-09-30) Jadav, Nilesh Kumar; Rathod, Tejal; Gupta, Rajesh; Tanwar, Sudeep; Kumar, Neeraj; Iqbal, Rahat; Atalla, Shadi; Mohammad, Hijji; Al-Rubaye, SabaThe modern warfare scenario has immense challenges that can risk personnel's lives, highlighting the need for data acquisition to win a military operation successfully. In this context, unmanned aerial vehicles (UAVs) play a significant role by covertly acquiring reconnaissance data from an enemy location to make the friendly troops aware. The acquired data is mission-critical and needs to be secured from the intruders, which can implicitly manipulate it for their benefit. Moreover, UAVs collect a large amount of data, including high-definition images and surveillance videos; handling such a massive amount of data is a bottleneck on traditional communication networks. To mitigate these issues, this article proposes a blockchain and machine learning (ML)-based secure and intelligent UAV communication underlying sixth-generation (6G) networks, that is, Block-USB. The proposed system refrain the disclosure of highly-sensitive military operations from intruders (either a rogue UAV or a malicious controller). The proposed system uses off-chain storage, that is, Interplanetary file system (IPFS), to improve the blockchain storage capacity. We also present a case study on securing UAV-based military operations by considering multiple scenarios considering controller/UAV malicious. The performance of the proposed system outperforms the traditional baseline 4G/5G and non IPFS-based systems in terms of classification accuracy, communication latency, and data scalability.Item Open Access Communication network architecture with 6G capabilities for urban air mobility(IEEE, 2024-02-28) Al-Rubaye, Saba; Conrad, Christopher; Tsourdos, AntoniosAs the demand for urban air mobility (UAM) increases, a robust communication, navigation, and surveillance (CNS) network architecture is needed to support the integration of sustainable UAM vehicles and technologies. Specifically, a new digital communication infrastructure is imperative to support increased levels of digitisation and autonomy within the aviation industry. This infrastructure must remain compatible with existing technologies, while enabling the integration of future 6G systems. This paper thereby discusses the communication challenges and opportunities associated with UAM integration. Potential communication technologies and standards needed to support UAM operations are presented and consolidated into a unified communication architecture with ground-, air-, and satellite-based infrastructure. The functional requirements of this architecture are also discussed, to enable seamless communication between UAM vehicles, air traffic control, and other ground- or air-based systems. Notably, 6G is highlighted as a key enabler of dense and sustainable UAM operations with high data traffic demands. A simple link budget analysis for a 6G air-to-ground data link in a green urban environment is thereby performed, em-phasising the infrastructural development necessary to support 6G roll-out. These findings pave the way for a more sustainable and accessible UAM transportation system, backed by a secure and reliable communication infrastructure.Item Open Access Developing secure hardware for UAV authorisation using lightweight authentication(IEEE, 2023-11-10) Sen, Muhammet A.; Al-Rubaye, Saba; Tsourdos, AntoniosIn this research, a unique authorisation strategy for unmanned aerial vehicle (UAV) systems is presented. UAVs are being used more frequently in a variety of military and civilian missions, including observation, reconnaissance, transportation, search and rescue, traffic surveillance, weather forecasting, and logistics operations. To address the need for secure authorization in UAV networks, we propose an innovative scheme that leverages Physically Unclonable Functions (PUFs) within the Flying Ad-Hoc Network (FANET) structure. Our plan strengthens the security and integrity of UAV operations by incorporating PUF technology. The suggested authorization architecture is appropriate for the dynamic and resource-constrained FANET context because it ensures effective and trustworthy authentication while minimising computational cost. Through this study, we hope to further the creation of reliable permission systems for UAV systems used in both military and civilian applications.Item Open Access Differentially-private federated intrusion detection via knowledge distillation in third-party IoT systems of smart airports(IEEE, 2023-10-24) Chen, Yang; Al-Rubaye, Saba; Tsourdos, Antonios; Baker, Lawrence; Gillingham, ColinWith the increasing deployment of IoT and Industry 4.0, the federated learning system was presented to preserve the privacy between the third-party IoT systems and the security operation center in smart airports. Nonetheless, the extremely skewed distribution of cyber threats increases the complexity of intrusion detection system (IDS) in smart airports, while privacy preservation limits the utility of IDS in the process of server model update. In this article, we have devised a knowledge distillation (KD)-based Convolutional Neural Network and Gated Recurrent Unit (CNN-GRU) model to improve the accuracy of multiple intrusion detection. In addition, the tradeoff between privacy and accuracy is achieved by denoising the adaptive parameter update mechanism to upgrade the optimizer of Differentially-Private (DP) Federated IDS. The results indicate high effectiveness and robustness of DP Federated KD-based IDS for third-party IoT systems of a smart airport.Item Open Access Digital twin analysis to promote safety and security in autonomous vehicles(IEEE, 2021-03-31) Almeaibed, Sadeq; Al-Rubaye, Saba; Tsourdos, Antonios; Avdelidis, Nicolas PeterWith the new industrial revolution of digital transformation, more intelligence and autonomous systems can be adopted in the manufacturing transportation processes. Safety and security of autonomous vehicles (AVs) have obvious advantages of reducing accidents and maintaining a cautious environment for drivers and pedestrians. Therefore, the transformation to data-driven vehicles is associated with the concept of digital twin, especially within the context of AV design. This also raises the need to adopt new safety designs to increase the resiliency and security of the whole AV system. To enable secure autonomous systems for smart manufacturing transportation in an end-to-end fashion, this article presents the main challenges and solutions considering safety and security functions. This article aims to identify a standard framework for vehicular digital twins that facilitate the data collection, data processing, and analytics phases. To demonstrate the effectiveness of the proposed approach, a case study for a vehicle follower model is analyzed when radar sensor measurements are manipulated in an attempt to cause a collision. Perceptive findings of this article can pave the way for future research aspects related to employing digital twins in the AV industry.Item Open Access Digital twin development for the airspace of the future(MDPI, 2023-07-23) Souanef, Toufik; Al-Rubaye, Saba; Tsourdos, Antonios; Ayo, Samuel; Panagiotakopoulos, DimitriosThe UK aviation industry is committed to achieving net zero emissions by 2050 through sustainable measures and one of the key aspects of this effort is the implementation of Unmanned Traffic Management (UTM) systems. These UTM systems play a crucial role in enabling the safe and efficient integration of unmanned aerial vehicles (UAVs) into the airspace. As part of the Airspace of the Future (AoF) project, the development and implementation of UTM services have been prioritised. This paper aims to create an environment where routine drone services can operate safely and effectively. To facilitate this, a digital twin of the National Beyond Visual Line of Sight Experimentation Corridor has been created. This digital twin serves as a virtual replica of the corridor and allows for the synthetic testing of unmanned traffic management concepts. The implementation of the digital twin involves both simulated and hybrid flights with real drones. Simulated flights allow for the testing and refinement of UTM services in a controlled environment. Hybrid flights, on the other hand, involve the integration of real drones into the airspace to assess their performance and compatibility with the UTM systems. By leveraging the capabilities of UTM systems and utilising the digital twin for testing, the AoF project aims to advance the development of safer and more efficient drone operations. The Experimentation Corridor has been developed to simulate and test concepts related to managing unmanned traffic. The paper provides a detailed account of the implementation of the digital twin for the AoF project, including simulated and hybrid flights involving real drones.Item Open Access Digital twins based intelligent state prediction method for maneuvering-target tracking(IEEE, 2023-08-30) Liu, Jingxian; Yan, Junjie; Wan, Dehuan; Li, Xuran; Al-Rubaye, Saba; Al-Dulaimi, Anwer; Quan, ZhiManeuvering-target tracking has always been an important and challenge work because the unknown and changeable motion-models can easily lead to the failure of model-driven target tracking. Recently, many neural network methods are proposed to improve the tracking accuracy by constructing direct mapping relationships from noisy observations to target states. However, limited by the coverage of training data, those data-driven methods suffer other problems, such as weak generalization abilities and unstable tracking effects. In this paper, a digital twin system for maneuvering-target tracking is built, and all kinds of simulated data are created with different motion-models. Based on those data, the features of noisy observations and their relationship to target states are found by two specially designed neural networks: one eliminates the observation noises and the other one predicts the target states according to the noise-limited observations. Combining the above two networks, the state prediction method is proposed to intelligently predict targets by understanding the information of motion-model hidden in noisy observations. Simulation results show that, in comparison with the state-of-the-art model-driven and data-driven methods, the proposed method can correctly and timely predict the motion-models, increase the tracking generalization ability and reduce the tracking root-mean-squared-error by over 50% in most of maneuvering-target tracking scenes.Item Open Access Enabling digital grid for industrial revolution: self-healing cyber resilient platform(IEEE, 2019-05-15) Al-Rubaye, Saba; Rodriguez, Jonathan; Al-Dulaimi, Anwer; Mumtaz, Shahid; Rodrigues, Joel J. P. C.The key market objectives driving digital grid development are to provide sustainable, reliable and secure network systems that can support variety of applications against any potential cyber attacks. Therefore, there is an urgent demand to accelerate the development of intelligent Software-Defined Networking (SDN) platform that can address the tremendous challenges of data protection for digital resiliency. Modern grid technology tends to adopt distributed SDN controllers for further slicing power grid domain and protect the boundaries of electric data at network edges. To accommodate these issues, this article proposes an intelligent secure SDN controller for supporting digital grid resiliency, considering management coordination capability, to enable self-healing features and recovery of network traffic forwarding during service interruptions. A set of advanced features are employed in grid controllers to configure the network elements in response to possible disasters or link failures. In addition, various SDN topology scenarios are introduced for efficient coordination and configurations of network domains. Finally, to justify the potential advantages of intelligent secure SDN system, a case study is presented to evaluate the requirements of secure digital modern grid networks and pave the path towards the next phase of industry revolution.Item Open Access Exploiting impacts of antenna selection and energy harvesting for massive network connectivity(IEEE, 2021-08-18) Van Nguyen, Minh-Sang; Do, Dinh-Thuan; Al-Rubaye, Saba; Mumtaz, Shahid; Al-Dulaimi, Anwer; Dobre, OctaviaAs a new energy saving approach for green communications, energy harvesting (EH) could be suitable technique to facilitate massive connections for large number of devices in such networks. The spectrum shortage occurs in huge number of devices which access with small-cell and macro-cell networks. To tackle these challenges, we develop a tractable framework relying on prominent techniques such as non-orthogonal multiple access (NOMA), antenna selection and energy harvesting. In this paper, we aim at practical scenarios of small cell networks by jointly evaluating capable of interference management and EH. We benefit from transmission approaches including full duplex (FD) and bi-directional transmission to improve the main performance system metrics such as outage probability and throughput. Three useful schemes are explored by considering EH and inter-cell interference. We derive the closed-form and asymptotic expressions for system metrics. We then perform extensive simulations with different system configurations to confirm the effectiveness of the proposed small-cell NOMA systems.Item Open Access A framework of network connectivity management in multi-clouds infrastructure(IEEE, 2019-02-21) Al-Dulaimi, Anwer; Mumtaz, Shahid; Al-Rubaye, Saba; Zhang, Siming; Lin, ChihThe 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.
- «
- 1 (current)
- 2
- 3
- »