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: 2318635
• 负责人: Sagnika Ghosh
• 金额: $ 12万
• 依托单位: Tennessee State University
• 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=2318635&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的先进功能将影响医疗、教育、娱乐、物联网（IoT）、自动驾驶汽车和智慧城市等多个行业。该研究的目的是创建能够有效管理连接到5G网络的物联网设备的系统。管理具有不同需求的大量物联网设备是一项复杂的任务，这使得手动管理具有挑战性。一些设备需要快速的数据传输来进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的响应时间来完成自动驾驶汽车或监控设备等任务。解决这些问题的方法是网络切片，将网络分成更小的部分来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地管理网络资源，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，它简化了网络，降低了成本，节省了能源，平衡了工作负载，优化了移动性，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中代表性不足的背景的学生，该项目旨在弥合美国各机构之间的差距。该项目将培养来自少数民族大学和边缘社区的下一代学者，并帮助在5G和强化学习（RL）领域发展劳动力。项目负责人还将接触到K-12，以促进教育，并与包括女性在内的各种学生接触。该项目的目标是设计一个框架，用于在大规模网络中利用大规模多输入多输出（MIMO）的RL技术，自动化支持5g的物联网设备的端到端资源管理。5G网络中各种用例、设备和应用的多样化需求，使得人工操作成本高、难度大、效率低。该项目将考虑灵活性，以确保网络能够快速适应不断变化的需求。它旨在降低网络的复杂性和成本，节约网络能源，优化负载均衡和移动性，简化资源管理。研究的范围是a)使用大规模MIMO为物联网设备设计5G网络切片，b)开发RL模型以解决大规模5G网络中物联网设备的编排问题，以及c)将RL解决方案集成到大规模MIMO网络切片的5G物联网网络中。5G网络切片方法将根据每个切片的特定需求进行资源分配，并通过采用大规模MIMO技术和RL模型，最大限度地降低运营费用（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。