Browsing by Author "Al-Dulaimi, Anwer"

Now showing 1 - 7 of 7
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    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.
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    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, Zhi
    Maneuvering-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.
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    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.
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    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, Octavia
    As 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.
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    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, Chih
    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.
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    A novel mapping technique for ray tracer to system-level simulation
    (Elsevier, 2019-12-02) Awais Khan, Muhammad; Adeshina Busari, Sherif; Mohammed Saidul Huq, Kazi; Mumtaz, Shahid; Al-Rubaye, Saba; Rodriguez, Jonathan; Al-Dulaimi, Anwer
    Simulations have become remarkably useful in evaluating the performance of new techniques and algorithms in communication networks. This is due to its comparative cost, time and complexity advantage over the analytical and field trial approaches. For large-scale networks, system-level simulators (SLS) are used to assess the performance of the systems. The SLS typically employs statistical channel models to characterize the propagation environment. However, the communication channels can be more accurately modeled using the deterministic ray tracing tools, though at the cost of higher complexity. In this work, we present a novel framework for a hybrid system that integrates both the ray tracer and the SLS. In the hybrid system, the channel strength in terms of the signal-to-noise ratio (SNR) is fed from the ray tracer to the SLS which then uses the values for further tasks such as resource allocation and the consequent performance evaluation. Using metrics such as user throughput and spectral efficiency, our results show that the hybrid system predicts the system performance more accurately than the baseline SLS without ray tracing. The hybrid system will thus facilitate the accurate assessment of the performance of next-generation wireless systems.
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    Power interchange analysis for reliable vehicle-to-grid connectivity
    (IEEE, 2019-08-21) Al-Rubaye, Saba; Al-Dulaimi, Anwer; Ni, Qiang
    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.