NeTS: Small: A Learning Approach to Managing Cellular Network Upgrades

NeTS：小型：管理蜂窝网络升级的学习方法

• 批准号: 1718089
• 负责人: Sanjay Shakkottai
• 金额: $ 15万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-10-01 至 2020-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1718089&HistoricalAwards=false
• 关键词: NeTS; Small; Learning; Approach; Managing

In order to provide a high-quality user experience, cellular service providers periodically upgrade their network software to introduce new features, fix software bugs, enhance quality of experience to users, or patch security vulnerabilities. The rollout of these upgrades need to be carefully managed, as tens of millions of customers rely on these networks for their daily activities (including emergency, navigation, and alert notifications). This project will develop techniques to manage cellular upgrades to minimize network disruption and improve user experience across the nation. Through collaboration with industry, the project will follow a fast track to technology transfer. The researchers will mentor undergraduate and graduate students, with a focus on actively recruiting female and other under-represented minority students.Prior to deploying a software upgrade fully over cellular network, field evaluations are conducted on a limited scale over a selected subset of base-stations. These field evaluations are typically cumbersome and can be time consuming; however, if done correctly they can help alleviate a lot of the deployment issues in terms of service quality degradation. Carefully selecting the specific base-stations to test, as well as the number of base-stations that are tested, is important -- too few could lead to problems during large-scale rollouts, and too many would be prohibitive in terms of cost. This project aims to develop learning-based approaches to automatically determine how many to select and where to conduct the upgrade field tests, where the learning methods are optimized to detect those base-station settings that could lead to failure of the upgrade. The researchers will evaluate the effectiveness of their approach using both real traces from major cellular networks as well as synthetic traces.

为了提供高质量的用户体验，蜂窝服务提供商定期升级其网络软件以引入新功能、修复软件错误、增强用户体验质量或修补安全漏洞。这些升级的推出需要仔细管理，因为数千万客户的日常活动（包括紧急情况，导航和警报通知）依赖于这些网络。该项目将开发管理蜂窝升级的技术，以最大限度地减少网络中断并改善全国范围内的用户体验。通过与工业界的合作，该项目将走上技术转让的快车道。研究人员将指导本科生和研究生，重点是积极招募女性和其他代表性不足的少数民族学生。在通过蜂窝网络全面部署软件升级之前，将在选定的基站子集上进行有限规模的现场评估。这些现场评估通常很麻烦，而且可能很耗时;但是，如果正确执行，它们可以帮助缓解服务质量下降方面的许多部署问题。仔细选择要测试的特定基站以及要测试的基站数量是很重要的--太少可能会在大规模推广期间导致问题，太多则会在成本方面令人望而却步。 该项目旨在开发基于学习的方法，以自动确定选择多少个和在何处进行升级现场测试，其中优化学习方法以检测可能导致升级失败的基站设置。研究人员将使用来自主要蜂窝网络的真实的痕迹以及合成痕迹来评估他们方法的有效性。

项目成果

Online Channel-state Clustering And Multiuser Capacity Learning For Wireless Scheduling

• DOI: 10.1109/infocom.2019.8737425
• 发表时间: 2019-04
• 期刊: IEEE INFOCOM 2019 - IEEE Conference on Computer Communications
• 作者: Isfar Tariq;Rajat Sen;G. Veciana;S. Shakkottai

Multi-dimensional Impact Detection and Diagnosis in Cellular Networks

• DOI: 10.1109/msn50589.2020.00093
• 发表时间: 2020-12
• 期刊: 2020 16th International Conference on Mobility, Sensing and Networking (MSN)
• 作者: M. Qureshi;L. Qiu;A. Mahimkar;Jian He;Ghufran Baig

Reflection: Automated test location selection for cellular network upgrades

反思：蜂窝网络升级的自动测试位置选择

• DOI: 10.1109/icnp.2017.8117553
• 发表时间: 2017
• 期刊: International Conference on Network Protocols
• 作者: Qureshi, Mubashir Adnan;Mahimkar, Ajay;Qiu, Lili;Ge, Zihui;Puthenpura, Sarat;Mir, Nabeel;Ahuja, Sanjeev
• 通讯作者: Ahuja, Sanjeev

Coordinating rolling software upgrades for cellular networks

协调蜂窝网络的滚动软件升级

• DOI: 10.1109/icnp.2017.8117537
• 发表时间: 2017
• 期刊: International Conference on Network Protocols
• 作者: Qureshi, Mubashir Adnan;Mahimkar, Ajay;Qiu, Lili;Ge, Zihui;Zhang, Max;Broustis, Ioannis
• 通讯作者: Broustis, Ioannis