Collaborative Research:CISE-MSI:RCBP-RF:CNS:Orchestration of Network Slicing for 5G-Enabled IoT Devices Using Reinforcement Learning

合作研究：CISE-MSI:RCBP-RF:CNS：使用强化学习为支持 5G 的物联网设备进行网络切片编排

• 批准号: 2318634
• 负责人: Kanwalinderjit Kaur
• 金额: $ 15.73万
• 依托单位: CSUB Auxiliary for Sponsored Programs Administration
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2318634&HistoricalAwards=false
• 关键词: Collaborative; Research; CISE; MSI; RCBP

Wireless communication is one of the most important mediums for transmitting information from one device to another. Most of the current wireless phones are supported by either 4G or 5G networks. 5G is meant to deliver higher data speeds, increased availability, and a uniform user experience to multiple users. 5G advanced capabilities will impact several industries including healthcare, education, entertainment, Internet of Things (IoT), autonomous vehicles, and smart cities. This research aims to create a system that can effectively manage IoT devices connected to the 5G network. Managing a multitude of IoT devices with diverse requirements is a complex task, making manual management challenging. Some devices require fast data transmission for activities like watching videos or playing virtual-reality games, while others need a quick response time for tasks like self-driving cars or monitoring devices. The solution to these problems is network slicing which involves dividing the network into smaller parts to handle different types of devices and services. However, the challenges inherent to network slicing are efficiently managing network resources, coordinating, and optimizing different parts of the network. This project addresses these challenges by designing a system that can automatically manage the resources of 5G-enabled IoT devices. The potential benefits of this approach are that it simplifies the network and reduces cost, saves energy, balances the workload, optimizes mobility, and makes the network easier to manage. This research advances the field by laying a solid groundwork for studying machine learning and network automation in devices that are part of the 5G-enabled IoT network. Furthermore, by employing and mentoring students from underrepresented backgrounds in STEM, this project will aim to bridge the gap in institutions across the US. This project will train the next generation of scholars from minority-serving universities and marginalized communities and help in workforce development in the fields of 5G and reinforcement learning (RL). The project leaders will also reach out to K-12 to promote education and engage with a diverse range of students, including women.The goal of this project is to devise a framework for automating end-to-end resource management of 5G-enabled IoT devices that utilizes RL techniques with massive multiple-input multiple-output (MIMO) in large-scale networks. The diverse needs of various use cases, devices, and applications in 5G networks make manual operation costly, difficult, and inefficient. This project will consider agility to ensure that the network can quickly adapt to evolving requirements. It aims to decrease network complexity and cost, conserve network energy, optimize load balancing and mobility, and simplify resource management. The scope of the research is a) designing 5G network slicing using Massive MIMO for IoT devices, b) developing an RL model to solve orchestration problems of IoT devices in large-scale 5G networks, and c) integrating the RL solution into a Massive MIMO network sliced 5G-enabled IoT network. The 5G network-slicing approach will enable resource allocation to each slice considering its specific needs and provide networks-as-a-service by minimizing operational expenses (OPEX) and capital expenditure (CAPEX) by adopting the Massive MIMO technique and RL models. This approach will result in higher availability, a specified latency, faster speed, better security, and higher throughput of RL-enabled Massive MIMO 5G 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.

无线通信是将信息从一个设备传输到另一个设备的最重要的媒介之一。目前大多数无线手机都支持 4G 或 5G 网络。 5G 旨在为多个用户提供更高的数据速度、更高的可用性和统一的用户体验。 5G 的先进功能将影响多个行业，包括医疗保健、教育、娱乐、物联网 (IoT)、自动驾驶汽车和智慧城市。这项研究旨在创建一个能够有效管理连接到 5G 网络的物联网设备的系统。管理大量具有不同需求的物联网设备是一项复杂的任务，使得手动管理具有挑战性。一些设备需要快速数据传输来执行观看视频或玩虚拟现实游戏等活动，而另一些设备则需要快速响应时间来执行自动驾驶汽车或监控设备等任务。这些问题的解决方案是网络切片，它将网络划分为更小的部分以处理不同类型的设备和服务。然而，网络切片固有的挑战是有效管理网络资源、协调和优化网络的不同部分。该项目通过设计一个可以自动管理支持 5G 的物联网设备资源的系统来解决这些挑战。这种方法的潜在好处是简化网络、降低成本、节省能源、平衡工作负载、优化移动性并使网络更易于管理。这项研究为研究 5G 物联网设备中的机器学习和网络自动化奠定了坚实的基础，从而推动了该领域的发展。此外，通过雇用和指导来自 STEM 领域代表性不足的学生，该项目旨在缩小美国各地机构的差距。该项目将培训来自少数族裔大学和边缘化社区的下一代学者，并帮助 5G 和强化学习 (RL) 领域的劳动力发展。项目负责人还将接触 K-12，以促进教育并与包括女性在内的各类学生互动。该项目的目标是设计一个框架，用于自动化 5G 物联网的端到端资源管理在大规模网络中利用 RL 技术和大规模多输入多输出 (MIMO) 的设备。 5G 网络中各种用例、设备和应用程序的多样化需求使得手动操作成本高昂、困难且低效。该项目将考虑敏捷性，以确保网络能够快速适应不断变化的需求。它旨在降低网络复杂性和成本，节省网络能源，优化负载平衡和移动性，并简化资源管理。研究范围是 a) 使用 Massive MIMO 为 IoT 设备设计 5G 网络切片，b) 开发 RL 模型来解决大规模 5G 网络中 IoT 设备的编排问题，以及 c) 将 RL 解决方案集成到 Massive MIMO 中网络切片支持 5G 的物联网网络。 5G 网络切片方法将根据每个切片的具体需求进行资源分配，并通过采用大规模 MIMO 技术和 RL 模型，最大限度地减少运营支出 (OPEX) 和资本支出 (CAPEX)，提供网络即服务。这种方法将为支持 RL 的大规模 MIMO 5G 网络带来更高的可用性、指定的延迟、更快的速度、更好的安全性和更高的吞吐量。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势进行评估，被认为值得支持以及更广泛的影响审查标准。