Collaborative Research: CNS Core: Small: Extended Reality over Wireless Cellular Networks: Quality-of-Experience Analysis and Optimization

合作研究：CNS 核心：小型：无线蜂窝网络上的扩展现实：体验质量分析和优化

• 批准号: 2007635
• 负责人: Walid Saad
• 金额: $ 23万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2007635&HistoricalAwards=false
• 关键词: Collaborative; Research; CNS; Core; Small

Extended reality (XR) technologies, encompassing virtual reality, augmented reality, and mixed reality, will immerse users in virtual worlds with unprecedented interactivity across multiple domains ranging from gaming to sports and entertainment. However, to date, XR technologies have mostly relied on wired connectivity, which restricts their use to small spaces and narrows their application domains. In contrast, the goal of this research is to unleash the potential of XR technologies by marrying them with seamless wireless connectivity. As a result, this research will potentially enable users to use XR applications on-the-go without being confined to indoor environments and having to carry cumbersome cable-connected XR equipment.This goal is achieved by developing a holistic framework for enabling XR over wireless networks by symbiotically integrating rigorous theory with real-world communication system design considerations. A key component of this framework is the development of precise quality-of-experience metrics that combine objective wireless quality-of-service indicators with subjective XR user experience measures using tools from utility theory. These metrics are then integrated into novel resource management algorithms that merge concepts from reinforcement learning and reservoir computing to guarantee seamless XR quality-of-experience and adapt the network to XR user dynamics. The developed solutions are validated in realistic environments using a combination of simulations and user experiments.This research will contribute towards accelerating the deployment of wireless XR technologies and applications thus having tangible societal impacts. A well-crafted educational plan focused on wireless XR will be developed and will include curriculum development as well as involvement of students in research and hands-on projects. Broadening participation outreach events will be organized as part of existing summer camps and will include seminars and hands-on activities that can expose the community to wireless XR technologies, in general, and the outcomes of this research, in particular. Workshops focused on wireless XR will be organized for broad dissemination. Outcomes of this project will be posted at https://sites.google.com/view/nsfcns2008646project/homeThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

延展实境（XR）技术包括虚拟现实、增强现实和混合现实，将使用户沉浸在虚拟世界中，在从游戏到体育和娱乐的多个领域中实现前所未有的互动。然而，到目前为止，XR技术主要依赖于有线连接，这将其使用限制在小空间内，并缩小了其应用领域。相比之下，这项研究的目标是通过将XR技术与无缝无线连接相结合来释放XR技术的潜力。因此，这项研究将有可能使用户能够在移动中使用XR应用程序，而不必局限于室内环境，也不必携带笨重的电缆连接的XR设备。这一目标是通过将严格的理论与现实世界的通信系统设计考虑因素共生结合，开发一个在无线网络上实现XR的整体框架来实现的。该框架的一个关键组成部分是开发精确的体验质量指标，该指标使用效用理论的工具将联合收割机客观的无线服务质量指标与主观的XR用户体验指标相结合。然后，这些指标被集成到新的资源管理算法中，这些算法融合了强化学习和水库计算的概念，以保证无缝的XR体验质量，并使网络适应XR用户动态。开发的解决方案在现实环境中使用模拟和用户实验相结合进行验证。这项研究将有助于加速无线XR技术和应用的部署，从而产生切实的社会影响。一个精心设计的教育计划，重点是无线XR将开发，将包括课程开发以及学生参与研究和实践项目。扩大参与的外联活动将作为现有夏令营的一部分组织，并将包括研讨会和实践活动，这些活动可以使社区了解无线XR技术，特别是这项研究的成果。将举办以无线XR为重点的讲习班，以广泛传播。该项目的成果将公布在https://sites.google.com/view/nsfcns2008646project/homeThis奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

项目成果

Disentangling Learnable and Memorizable Data via Contrastive Learning for Semantic Communications

通过语义通信的对比学习来解开可学习和可记忆的数据

• 发表时间: 2022
• 期刊: Systems and Computer
• 作者: Chaccour, Christina;Saad, Walid
• 通讯作者: Saad, Walid

Joint Sensing and Communication for Situational Awareness in Wireless THz Systems

• DOI: 10.1109/icc45855.2022.9838764
• 发表时间: 2021-11
• 期刊: ICC 2022 - IEEE International Conference on Communications
• 作者: Christina Chaccour;W. Saad;Omid Semiari;M. Bennis;P. Popovski

Edge Continual Learning for Dynamic Digital Twins over Wireless Networks

通过无线网络实现动态数字孪生的边缘持续学习

• DOI: 10.1109/spawc51304.2022.9833928
• 发表时间: 2022
• 期刊: EEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC
• 作者: Hashash, Omar;Chaccour, Christina;Saad, Walid
• 通讯作者: Saad, Walid

Performance Optimization for Semantic Communications: An Attention-based Learning Approach

• DOI: 10.1109/globecom46510.2021.9685056
• 发表时间: 2021-12
• 期刊: 2021 IEEE Global Communications Conference (GLOBECOM)
• 作者: Yining Wang;Mingzhe Chen;W. Saad;Tao Luo;Shuguang Cui;H. Poor

The Roadmap to a Quantum-Enabled Wireless Metaverse: Beyond the Classical Limits

• DOI: 10.1109/actea58025.2023.10194017
• 发表时间: 2023-07
• 期刊: 2023 Fifth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA)
• 作者: Mahdi Chehimi;Omar Hashash;Walid Saad