CRII: CNS: Towards Robust and Efficient Dynamic Spectrum Sharing with Knowledge Transfer

CRII：CNS：通过知识转移实现稳健、高效的动态频谱共享

• 批准号: 2245918
• 负责人: LUSI LI
• 金额: $ 17.5万
• 依托单位: Old Dominion University Research Foundation
• 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=2245918&HistoricalAwards=false
• 关键词: CRII; CNS; Towards; Robust; Efficient; CRII; CNS; Towards; Robust; Efficient

The proliferation of wireless devices and the increasing demand for wireless services, coupled with inefficient spectrum allocation and limited spectrum availability, exacerbate the scarcity of wireless spectrum resources in next-generation (NextG) networks. This project focuses on the development of novel transfer learning (TL) frameworks for dynamic spectrum sharing (DSS) to enable knowledge transfer across users, environments, and wireless systems, offering viable approaches to intelligently utilize underutilized licensed spectrum more effectively. DSS wireless systems exhibit characteristics such as dynamic environments, heterogeneous networks, massive connections, interference, high communication overhead, limited computing and storage capacity, as well as security and privacy concerns, making it challenging to learn and leverage transferable knowledge. Moreover, achieving the desired performance of knowledge transfer often requires substantial amounts of high-quality training data, while transferring data knowledge may raise security and privacy issues, limiting adaptation and generalization to other tasks. Therefore, this project aims to explore novel TL strategies for learning transferable knowledge and addressing concerns related to robustness, efficiency, security, and privacy in DSS systems. A key thrust of the project involves a systematic investigation into the characteristics and parameters of target DSS wireless systems, alongside an exploration of the fundamental principles, theories, and unique challenges associated with knowledge transfer. These studies aim to bridge the gap between system characteristics and algorithm development. The research tasks include the following: (1) Design an ensemble evaluation scheme to assess the robustness, efficiency, security, and privacy of TL-based DSS frameworks. (2) Develop efficient TL-based DSS frameworks for adaptive spectrum sensing, selection, access, and handoff. (3) Create robust security and privacy TL strategies for monitoring, detecting, mitigating, and preventing various malicious attacks, while also protecting sensitive data. Concurrently, the research team is developing a Wireless Knowledge Transfer testbed that incorporates transferable knowledge, evaluation schemes, pre-trained TL models, attack knowledge databases, and security and privacy strategies. This testbed helps to facilitate and standardize research on knowledge reuse in wireless communication systems. The integration of research and education plans prepares the NextG workforce in the fields of DSS, artificial intelligence, transfer learning, and cybersecurity. Outreach activities establish connections between the DSS research, and K-12 students, minority groups, and college students through various learning approaches.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.

无线设备的扩散以及对无线服务的需求不断增长，再加上频谱分配和有限的频谱可用性，加剧了下一代（NextG）网络中无线光谱资源的稀缺性。该项目着重于动态频谱共享（DSS）的新型转移学习（TL）框架的开发，以使知识转移在用户，环境和无线系统之间，提供可行的方法来更有效地智能利用未充分利用的许可频谱。 DSS无线系统具有动态环境，异质网络，大规模连接，干扰，高通信开销，有限的计算和存储容量以及安全性和隐私问题，使学习和利用可转移知识的挑战。此外，实现所需的知识转移表现通常需要大量的高质量培训数据，同时传输数据知识可能会增加安全性和隐私问题，从而将适应和对其他任务的推广限制。因此，该项目旨在探索新颖的TL策略，以学习可转移的知识，并解决与DSS系统中与鲁棒性，效率，安全性和隐私相关的问题。 该项目的关键作用涉及对目标DSS无线系统的特征和参数进行系统的调查，并探索与知识转移相关的基本原理，理论和独特的挑战。这些研究旨在弥合系统特征与算法开发之间的差距。研究任务包括以下内容：（1）设计合奏评估方案，以评估基于TL的DSS框架的鲁棒性，效率，安全性和隐私。 （2）开发有效的基于TL的DSS框架，用于自适应频谱感测，选择，访问和交接。 （3）创建强大的安全性和隐私性TL策略，以监视，检测，缓解和防止各种恶意攻击，同时也保护敏感数据。同时，研究团队正在开发一个无线知识转移测试台，该测试台结合了可转移的知识，评估方案，预训练的TL模型，攻击知识数据库以及安全性和隐私策略。该测试床有助于促进和标准化无线通信系统中知识再利用的研究。研究和教育计划的整合为DSS，人工智能，转移学习和网络安全领域的NextG劳动力做好了准备。外展活动通过各种学习方法建立了DSS研究与K-12学生，少数群体和大学生之间的联系。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评估评估的评估来支持的。