Collaborative Research: SWIFT: SHIELD: A Software-Hardware Approach for Spectrum Coexistence with Rapid Interferer Learning, Detection, and Mitigation

合作研究：SWIFT：SHIELD：一种实现频谱共存并具有快速干扰源学习、检测和缓解的软件硬件方法

• 批准号: 2128530
• 负责人: Leandros Tassiulas
• 金额: $ 25万
• 依托单位: Yale University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2128530&HistoricalAwards=false
• 关键词: Collaborative; Research; SWIFT; SHIELD; Software

The last two decades have witnessed enormous increase in wireless data transfer, impacting every aspect our lives and our nation's economy. This has led to a spectrum crunch in frequency bands below 6 GHz that are the most useful for intermediate and long-range wireless communications. Even as new spectrum is allocated for different wireless networks and systems, satisfying the increasing demand of high data rates will lead to coexistence of active users (that generate signals) and passive users (that sense signals for weather sensing, astronomy, and other applications). Inevitably, interference occurs when these users share the same frequency band or when active users operate in bands close to that used by passive users. Therefore, there is a critical need for an approach to efficiently manage and reduce such interference, which will allow for further improved spectrum usage. This project will address this challenge and focus on a cross-layer software-hardware approach for spectrum coexistence with rapid interferer learning, detection, and mitigation (SHIELD). On a societal scale, the proposed research can improve spectrum utilization and increase access to in-demand wireless data without adversely impacting existing users, which will have direct economic impact. The broader impacts also include major outreach activities involving high school students and aiming at broadening the participation of women and underrepresented minorities, as well as incorporation of new hardware, software, and network architecture into undergraduate and graduate classes.Specifically, this interdisciplinary project will bridge the gap between the broad areas of integrated circuits, communications, networking, and machine learning, and will focuses on enabling rapid interferer learning, detection, and mitigation for spectrum coexistence based on the co-design of novel RF hardware and network control architecture. The main activities include: (i) extensive spectrum measurements and data collection for characterizing the spectrum usage and properties of potential interferers, (ii) development of a novel reconfigurable 0.4-4.0 GHz MIMO receiver architecture leveraging a concurrent auxiliary receiver for rapid interference detection and N-path sequence-mixing for nulling specific interferers, and (iii) design of an intelligent control plane, which integrates software-defined networking and machine learning techniques, for efficient spectrum monitoring, management, and resource allocation across spatially distributed receivers. The developed hardware and software will be evaluated in the lab setting and in real-world environments through their integration in a city-scale wireless testbed.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.

在过去的二十年里，无线数据传输出现了巨大的增长，影响到了我们生活的方方面面和我们国家的经济。这导致了6 GHz以下频段的频谱紧缩，这些频段对中长距离无线通信最有用。尽管为不同的无线网络和系统分配了新的频谱，但满足日益增长的高数据速率需求将导致主动用户(产生信号)和被动用户(感知天气传感、天文和其他应用的信号)共存。当这些用户共享相同的频段时，或者当主动用户在与被动用户使用的频段接近的频段工作时，不可避免地会发生干扰。因此，迫切需要一种方法来有效地管理和减少此类干扰，从而进一步提高频谱利用率。该项目将应对这一挑战，并将重点放在跨层软件-硬件方法上，以实现频谱共存和快速干扰学习、检测和缓解(屏蔽)。在社会规模上，拟议的研究可以提高频谱利用率，增加对按需无线数据的访问，而不会对现有用户产生负面影响，这将产生直接的经济影响。更广泛的影响还包括主要的外展活动，涉及高中生，旨在扩大女性和代表性不足的少数群体的参与，以及将新的硬件、软件和网络架构纳入本科生和研究生课程。具体地说，这一跨学科项目将弥合集成电路、通信、网络和机器学习等广泛领域之间的差距，并将专注于基于新型射频硬件和网络控制架构的共同设计，实现快速干扰学习、检测和缓解频谱共存。主要活动包括：(I)广泛的频谱测量和数据收集，以表征潜在干扰的频谱使用和特性，(Ii)开发一种新型的可重新配置的0.4-4.0 GHz MIMO接收器架构，利用并行辅助接收器进行快速干扰检测和N路径序列混合，以消除特定干扰，以及(Iii)设计智能控制平面，其集成了软件定义的联网和机器学习技术，用于跨空间分布的接收器进行高效的频谱监测、管理和资源分配。开发的硬件和软件将在实验室环境和真实环境中通过在城市规模的无线测试中进行集成进行评估。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和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Optimizing Sectorized Wireless Networks: Model, Analysis, and Algorithm

• DOI: 10.1145/3565287.3610272
• 发表时间: 2023-08
• 期刊: Proceedings of the Twenty-fourth International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing
• 作者: Panagiotis Promponas;Ting-Chen Chen-Ting-Chen-Chen-2117181382;L. Tassiulas

Robust SDN Synchronization in Mobile Networks Using Deep Reinforcement and Transfer Learning

• DOI: 10.1109/icc45041.2023.10278580
• 发表时间: 2023-05
• 期刊: ICC 2023 - IEEE International Conference on Communications
• 作者: Akrit Mudvari;Konstantinos Poularakis;L. Tassiulas

Network Slicing: Market Mechanism and Competitive Equilibria

• DOI: 10.1109/infocom53939.2023.10228856
• 发表时间: 2023-01
• 期刊: IEEE INFOCOM 2023 - IEEE Conference on Computer Communications
• 作者: Panagiotis Promponas;L. Tassiulas