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

无线设备的激增和对无线服务的日益增长的需求，加上低效的频谱分配和有限的频谱可用性，加剧了下一代（NextG）网络中无线频谱资源的稀缺性。该项目的重点是开发用于动态频谱共享（DSS）的新型迁移学习（TL）框架，以实现跨用户，环境和无线系统的知识转移，提供更有效地智能利用未充分利用的许可频谱的可行方法。DSS无线系统具有动态环境、异构网络、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和隐私问题等特性，这使得学习和利用可转移知识变得具有挑战性。此外，实现所需的知识转移性能通常需要大量高质量的训练数据，而转移数据知识可能会引发安全和隐私问题，从而限制对其他任务的适应和概括。因此，这个项目的目的是探索新的TL策略学习可转移的知识和解决DSS系统中的鲁棒性，效率，安全性和隐私相关的问题。 该项目的一个关键重点是对目标DSS无线系统的特性和参数进行系统研究，同时探索与知识转移相关的基本原理、理论和独特挑战。这些研究旨在弥合系统特性和算法开发之间的差距。研究工作包括：（1）设计一个集成评估方案，对基于TL的DSS框架的鲁棒性、效率、安全性和隐私性进行评估。(2)开发高效的基于TL的DSS框架，用于自适应频谱感知、选择、接入和切换。(3)创建强大的安全和隐私TL策略，用于监控、检测、缓解和防止各种恶意攻击，同时保护敏感数据。同时，研究团队正在开发一个无线知识转移测试平台，该平台整合了可转移知识、评估方案、预先训练的TL模型、攻击知识数据库以及安全和隐私策略。该试验平台有助于促进和规范无线通信系统中的知识重用研究。研究和教育计划的整合为NextG在DSS，人工智能，迁移学习和网络安全领域的劳动力做好了准备。该奖项反映了NSF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。

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.