620 research outputs found

    EdgeRIC: Empowering Realtime Intelligent Optimization and Control in NextG Networks

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    Radio Access Networks (RAN) are increasingly softwarized and accessible via data-collection and control interfaces. RAN intelligent control (RIC) is an approach to manage these interfaces at different timescales. In this paper, we develop a RIC platform called RICworld, consisting of (i) EdgeRIC, which is colocated, but decoupled from the RAN stack, and can access RAN and application-level information to execute AI-optimized and other policies in realtime (sub-millisecond) and (ii) DigitalTwin, a full-stack, trace-driven emulator for training AI-based policies offline. We demonstrate that realtime EdgeRIC operates as if embedded within the RAN stack and significantly outperforms a cloud-based near-realtime RIC (> 15 ms latency) in terms of attained throughput. We train AI-based polices on DigitalTwin, execute them on EdgeRIC, and show that these policies are robust to channel dynamics, and outperform queueing-model based policies by 5% to 25% on throughput and application-level benchmarks in a variety of mobile environments.Comment: 16 pages, 15 figure

    Modeling of On-line Traffic Control and Management Network for Operational and Communication Performance Evaluation

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    Communication systems are the backbone of every effective and reliable traffic control and management application. While traditional fiber optics and telephone communications have long been used in managing and controlling highway traffic, wireless communication technology shows great promise as an alternative solution in traffic management applications due to their suitability for deployment in rural areas, and their flexibility and cost-effectiveness for system expansion. However, the detailed characteristics of various wireless communication technologies and real performance in the field have not been systematically studied. To augment this existing knowledge so that traffic professionals may better utilize these technologies to improve traffic safety, mobility and efficiency, this study aims to 1) identify existing wireless communication technologies used in ITS, and potential wireless communication alternatives that can be widely used in ITS, 2) evaluate the performance, cost and reliability of existing and potential wireless communication technologies in supporting on-line traffic control and management functions, and 3) apply benefit-cost analysis to identify the impacts of using these wireless technologies to support on-line traffic management. To achieve these research objectives, the author first conducted an interview to discover the specifications of existing communication infrastructures deployed for various ITS related applications and the usage of wireless technologies in different states. Moreover, the author proposed a network design process that considered wireless coverage range and network topology, followed with case studies utilizing Wireless Fidelity (WiFi) and Worldwide Interoperability for Microwave Access (WiMAX) technologies to support a traffic surveillance system in seven metropolitan areas throughout South Carolina. Field tests were conducted to evaluate the performance and reliability of wireless transmissions between adjacent sensor nodes. After that, the author applied a communication simulator, ns-2, to compare the communication performance of a traffic sensor network with WiFi and WiMAX technologies under infrastructure and mesh topologies, and environmental conditions. Based on these simulation results, the author conducted performance-cost analysis for these selected technologies and topologies. The WiFi field test results indicated that wireless communication performance between two traffic sensors significantly degrades after 300 ft; this distance, however, may vary with the modulation rates and transmission power upon which the system operates. WiMAX nomadic test suggested that line-of-sight (LOS) greatly affects the connectivity level. Moreover, the capabilities and the performance of the WiMAX network are sometimes affected by the characteristics of the client radio. The simulation analysis and benefit-cost analysis indicated a WiFi mesh network solution has the highest throughput-cost ratio, 109 bits/dollar for supporting traffic surveillance systems, while the WiMAX infrastructure option provides the greatest amount of excess bandwidth, 9.15Mbps per device, which benefits the system\u27s future expansion. This dissertation provides an important foundation for further investigation of the performance and reliability of different wireless technologies. In addition, research results presented in this dissertation will benefit transportation agencies and other stakeholders in evaluating and selecting wireless communication options for different traffic control and management applications

    Embracing Visual Experience and Data Knowledge: Efficient Embedded Memory Design for Big Videos and Deep Learning

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    Energy efficient memory designs are becoming increasingly important, especially for applications related to mobile video technology and machine learning. The growing popularity of smart phones, tablets and other mobile devices has created an exponential demand for video applications in today?s society. When mobile devices display video, the embedded video memory within the device consumes a large amount of the total system power. This issue has created the need to introduce power-quality tradeoff techniques for enabling good quality video output, while simultaneously enabling power consumption reduction. Similarly, power efficiency issues have arisen within the area of machine learning, especially with applications requiring large and fast computation, such as neural networks. Using the accumulated data knowledge from various machine learning applications, there is now the potential to create more intelligent memory with the capability for optimized trade-off between energy efficiency, area overhead, and classification accuracy on the learning systems. In this dissertation, a review of recently completed works involving video and machine learning memories will be covered. Based on the collected results from a variety of different methods, including: subjective trials, discovered data-mining patterns, software simulations, and hardware power and performance tests, the presented memories provide novel ways to significantly enhance power efficiency for future memory devices. An overview of related works, especially the relevant state-of-the-art research, will be referenced for comparison in order to produce memory design methodologies that exhibit optimal quality, low implementation overhead, and maximum power efficiency.National Science FoundationND EPSCoRCenter for Computationally Assisted Science and Technology (CCAST

    Mobile journalism at RTP: production of news - using the smartphone as a tool for news production

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    The goal of this paper is to show case a practical resolution for the integration a ta greater scale of the Mobile Journalism philosophy, both in the production and in the consumption of news. The production-side concerns the use of the smartphone and other light equipment in the production of news, while the consumption-side concerns how the news are displayed and consumed on a smartphone. This work project was realized in syndication with RTP and was adjust and tailored to its respective needs, resources and objectives. In order to achieve this goal, several analysis were developed to address the external and internal environment, identifying the opportunities and threats of the broad casting industry and the strenggic recommendations that ensures a work able dissemination plan for Mobile Journalism

    Machine Learning-Powered Management Architectures for Edge Services in 5G Networks

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    Graphics Insertions into Real Video for Market Research

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    A deep reinforcement learning-based resource management scheme for SDN-MEC-supported XR applications

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    The Multi-Access Edge Computing (MEC) paradigm provides a promising solution for efficient computing services at edge nodes, such as base stations (BS), access points (AP), etc. By offloading highly intensive computational tasks to MEC servers, critical benefits in terms of reducing energy consumption at mobile devices and lowering processing latency can be achieved to support high Quality of Service (QoS) to many applications. Among the services which would benefit from MEC deployments are eXtended Reality (XR) applications which are receiving increasing attention from both academia and industry. XR applications have high resource requirements, mostly in terms of network bandwidth, computation and storage. Often these resources are not available in classic network architectures and especially not when XR applications are run by mobile devices. This paper leverages the concepts of Software Defined Networking (SDN) and Network Function Virtualization (NFV) to propose an innovative resource management scheme considering heterogeneous QoS requirements at the MEC server level. The resource assignment is formulated by employing a Deep Reinforcement Learning (DRL) technique to support high quality of XR services. The simulation results show how our proposed solution outperforms other state-of-the-art resource management-based schemes
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