Collaborative Research: NeTS: JUNO3: SWIFT: Softwarization of Intelligence for Efficient 6G Mobile Networks

未来的无线网络预计将在60ghz WiGig频段运行，以支持虚拟现实和增强现实等苛刻的应用。然而，WiGig频段的无线链路经常中断。这反过来又降低了移动用户感知到的服务质量。为了在WiGig频段实现稳定和高质量的无线连接，这个美日联合项目提出了一个智能层，该智能层可以学习无线链路和网络流量负载的条件，并做出明智的网络管理决策，以减轻网络中断。该层将支持超低延迟的超高数据速率，从而确保良好的应用性能和用户满意度，尽管环境中存在广泛的动态范围。通过实现稳定、高质量的WiGig连接，该项目将实现第六代（6G）应用，如触觉通信、增强现实、虚拟现实、远程手术等。因此，该项目广泛影响了社会的几个方面，并增强了美国的经济竞争力。未来，超高吞吐量和超低延迟应用预计将占移动数据流量的90%。因此，6G无线网络将在不拥挤的高频段运行，例如60ghz WiGig。由于衍射能力有限，WiGig波段的无线链路经常中断。为了保持稳定的无线连接和用户移动性，这个美日联合项目提出了SWIFT，即高效6G移动网络的智能软件化。SWIFT扩展了SDN （software defined network）架构，在SDN控制平面集成了基于人工智能（AI）的层，实现了高效的资源管理决策。该项目包括以下研究重点：(1)在考虑动态场景的情况下，WiGig网络中无线信道增益和流量负载的生成、验证和表征；(2)基于深度机器学习技术的WiGig网络无线信道增益和流量负载高效预测模型的开发；(3)基于强化学习工具的基于人工智能的WiGig网络信道分配策略的开发。该项目计划使用最先进的模拟室内移动WiGig网络的测试平台，对SWIFT框架进行概念验证和性能验证。该奖项反映了美国国家科学基金会的法定使命，并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，被认为值得支持。

Future wireless networks are expected to operate in the 60 GHz WiGig frequency band to support demanding applications such as virtual reality and augmented reality. However, the wireless links at the WiGig band suffer from frequent outages. This in turn degrades the perceived service quality for mobile users. To enable stable and high-quality wireless connectivity in the WiGig band, this joint US-Japan project proposes an intelligent layer that learns the conditions of the wireless links and network traffic load and makes informed network management decisions to mitigate network disruption. This layer will support ultra-high data rates with low latency, thus ensuring good application performance and user satisfaction despite the wide dynamic range present in the environment. By enabling stable and high-quality WiGig connections, this project will enable sixth generation (6G) applications such as haptic communication, augmented reality, virtual reality, remote surgery, etc. Hence, this project broadly impacts several aspects of society and enhances the economic competitiveness of the U.S.In the future, ultra-high throughput and ultra-low delay applications are anticipated to constitute 90% of mobile data traffic. As a result, 6G wireless networks will operate in the uncongested high frequency bands, e.g., the 60 GHz WiGig. Due to limited diffraction capabilities, the wireless links at the WiGig band suffer from frequent outages. To maintain stable wireless connectivity with user mobility, this joint US-Japam project proposes SWIFT, SoftWarization of Intelligence for eFficient 6G mobile neTworks. SWIFT extends the software defined networking (SDN) architecture by integrating an artificial intelligence (AI)-based layer in the SDN control plane to enable efficient resource management decisions. The project includes the following research thrusts: (1) Generation, validation, and characterization of wireless channel gain and traffic load in WiGig networks while considering dynamic scenarios; (2) Development of efficient prediction models for wireless channel gain and traffic load in WiGig networks based on deep machine learning techniques; (3) Development of AI-based strategy for channel assignment in WiGig networks based on reinforcement learning tools. The project plans a proof-of-concept implementation and performance validation of the SWIFT framework using a state-of-the-art testbed that mimics indoor mobile WiGig networks.This 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.