17 research outputs found

    Intelligent beam blockage prediction for seamless connectivity in vision-aided next-generation wireless networks

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    The upsurge in wireless devices and real-time service demands force the move to a higher frequency spectrum. Millimetre-wave (mmWave) and terahertz (THz) bands combined with the beamforming technology offer significant performance enhancements for future wireless networks. Unfortunately, shrinking cell coverage and severe penetration loss experienced at higher spectrum render mobility management a critical issue in high-frequency wireless networks, especially optimizing beam blockages and frequent handover (HO). Mobility management challenges have become prevalent in city centres and urban areas. To address this, we propose a novel mechanism driven by exploiting wireless signals and on-road surveillance systems to intelligently predict possible blockages in advance and perform timely HO. This paper employs computer vision (CV) to determine obstacles and users’ location and speed. In addition, this study introduces a new HO event, called block event (BLK), defined by the presence of a blocking object and a user moving towards the blocked area. Moreover, the multivariate regression technique predicts the remaining time until the user reaches the blocked area, hence determining best HO decision. Compared to conventional wireless networks without blockage prediction, simulation results show that our BLK detection and proactive HO algorithm achieves 40% improvement in maintaining user connectivity and the required quality of experience (QoE)

    Improved handover decision scheme for 5g mm-wave communication: optimum base station selection using machine learning approach.

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    A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Information and Communication Science and Engineering of the Nelson Mandela African Institution of Science and TechnologyThe rapid growth in mobile and wireless devices has led to an exponential demand for data traf fic and exacerbated the burden on conventional wireless networks. Fifth generation (5G) and beyond networks are expected to not only accommodate this growth in data demand but also provide additional services beyond the capability of existing wireless networks, while main taining a high quality-of-experience (QoE) for users. The need for several orders of magnitude increase in system capacity has necessitated the use of millimetre wave (mm-wave) frequencies as well as the proliferation of low-power small cells overlaying the existing macro-cell layer. These approaches offer a potential increase in throughput in magnitudes of several gigabits per second and a reduction in transmission latency, but they also present new challenges. For exam ple, mm-wave frequencies have higher propagation losses and a limited coverage area, thereby escalating mobility challenges such as more frequent handovers (HOs). In addition, the ad vent of low-power small cells with smaller footprints also causes signal fluctuations across the network, resulting in repeated HOs (ping-pong) from one small cell (SC) to another. Therefore, efficient HO management is very critical in future cellular networks since frequent HOs pose multiple threats to the quality-of-service (QoS), such as a reduction in the system throughput as well as service interruptions, which results in a poor QoE for the user. How ever, HO management is a significant challenge in 5G networks due to the use of mm-wave frequencies which have much smaller footprints. To address these challenges, this work in vestigates the HO performance of 5G mm-wave networks and proposes a novel method for achieving seamless user mobility in dense networks. The proposed model is based on a double deep reinforcement learning (DDRL) algorithm. To test the performance of the model, a com parative study was made between the proposed approach and benchmark solutions, including a benchmark developed as part of this thesis. The evaluation metrics considered include system throughput, execution time, ping-pong, and the scalability of the solutions. The results reveal that the developed DDRL-based solution vastly outperforms not only conventional methods but also other machine-learning-based benchmark techniques. The main contribution of this thesis is to provide an intelligent framework for mobility man agement in the connected state (i.e HO management) in 5G. Though primarily developed for mm-wave links between UEs and BSs in ultra-dense heterogeneous networks (UDHNs), the proposed framework can also be applied to sub-6 GHz frequencies

    Location, Location, Location: Maximizing mmWave LAN Performance through Intelligent Wireless Networking Strategies

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    The main objective of this dissertation is to design and evaluate intelligent techniques to maximize mmWave wireless local-area network (WLAN) performance. To meet the ever-increasing data demand of various bandwidth-hungry applications, we propose techniques to enable consistently ultra-high-rate mmWave communication in the wireless environment. However, the weak diffraction of mmWave signals makes them extremely sensitive to blockage effects caused by real-world obstacles, and this is a primary challenge to overcome for the feasibility of mmWave communications. To this end, we exploit location sensitivity to explore robust mmWave WLAN designs that expedite the full realization of ubiquitous mmWave wireless connectivity. The techniques investigated to exploit location sensitivity are the use of multiple access points (APs), controlled mobility, AP-user association mechanisms, and environment-aware prediction We first develop optimal multi-AP planning approaches to maximize line-of-sight connectivity and aggregate throughput in mmWave WLANs, and then study multi-AP association mechanisms to achieve low-overhead and blockage-robust mmWave wireless communications among multiple users and multiple APs. Furthermore, we explore the potential benefits achievable from AP mobility technology, which yields insights on the best configurations of mobile APs. We also develop an environment-aware link-quality predictor to accurately derive dynamic mmWave link quality due to static blockages and small changes in device locations, which provides a basis for the development of anticipatory networking with proactive resource-allocation schemes. In a complementary direction for evaluating the performance of mmWave networks, we develop and implement advanced features for dense wireless networks that increasingly characterize many mmWave scenarios of interest in the widely-used network simulator ns-3, including a sparse cluster-based wireless channel model that statistically models multi-path components in mmWave WLANs.Ph.D

    Methods for Massive, Reliable, and Timely Access for Wireless Internet of Things (IoT)

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    Applications

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    Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications
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