CCSS: Secure Dual-Function Radar Communication Systems Assisted by Intelligent Reflecting Surfaces

CCSS：智能反射面辅助的安全双功能雷达通信系统

• 批准号: 2320568
• 负责人: Athina Petropulu
• 金额: $ 20万
• 依托单位: Rutgers University New Brunswick
• 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: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2320568&HistoricalAwards=false
• 关键词: CCSS; Secure; Dual; Function; Radar

As next-generation wireless systems strive to incorporate sensing capabilities alongside communication functionalities, the efficient allocation of spectrum between radar and communication functions has become a prominent focus. One promising solution is the utilization of dual-function radar-communication (DFRC) systems. These systems leverage a single device and a single waveform to enable simultaneous sensing and communication, resulting in improved spectrum utilization, hardware efficiency, and power savings. To further enhance the performance of DFRC systems in challenging channel conditions, an emerging technology called Intelligent Reflecting Surfaces (IRS) can be employed. IRS involves a planar array of numerous low-cost, real-time configurable elements that intelligently manipulate the transmitted waveform, creating a smart propagation environment. While DFRC systems excel at achieving high performance in both sensing and communication, they are vulnerable to potential eavesdroppers due to the embedded communication information within the probing waveform. This project aims to develop novel IRS-aided DFRC system designs that deliver reliable, high-rate information to the intended communication receiver while minimizing the information accessible to eavesdroppers. Successful implementation of secure DFRC systems will have wide-ranging benefits across applications such as autonomous driving vehicles, unmanned aerial vehicles, surveillance, search and rescue operations, and advanced manufacturing processes involving networked robots.This project addresses optimal IRS-aided DFRC system design and encompasses two thrusts. Thrust 1 considers design in dynamic environments, such as urban communication environments. A natural framework for dynamically optimizing the system parameters in that context is Deep Reinforcement Learning (RL), since it is adaptive, data-driven and does not require pre-annotated data. In Thrust 1, a novel and principled deep RL framework will be developed that incorporates domain knowledge and ensures convergence under gradient descent dynamics. An off-policy actor-critic approach will be considered, and the role of the Neural Tangent Kernel (NTK) of the critic network to the training stability of the deep RL algorithm and its performance to unseen or rarely experienced events will be investigated. Intelligent eavesdroppers will be addressed by formulating the performance optimization problem as a multi-agent reinforcement learning problem that considers both cooperation and competition. Thrust 2 considers DFRC systems transmitting Orthogonal Frequency Division Multiplexing (OFDM) waveforms and introduces the novel concept of Directional Modulation via Time-Modulated IRS (TM-IRS), which allows for more flexible secure system design. With TM-IRS, each element of the IRS periodically turns ON/OFF across OFDM symbols. By carefully designing the periodic activation pattern as well as the IRS parameters and the transmit antenna weights, the signal can be delivered intact in a desired direction while appearing scrambled in all other directions. TM-IRS offers a large number of degrees of freedom for system design, thus enhancing the secure operation of DFRC systems.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.

随着下一代无线系统努力将传感能力与通信功能结合起来，雷达和通信功能之间的频谱有效分配已成为一个突出的焦点。一个有前途的解决方案是利用双功能雷达通信（DFRC）系统。这些系统利用单个器件和单个波形来实现同时感测和通信，从而提高频谱利用率、硬件效率和节能。为了进一步增强DFRC系统在具有挑战性的信道条件下的性能，可以采用称为智能反射表面（IRS）的新兴技术。IRS涉及大量低成本、实时可配置元件的平面阵列，这些元件智能地操纵传输的波形，从而创建智能传播环境。虽然DFRC系统擅长在感测和通信方面实现高性能，但由于探测波形内嵌入的通信信息，它们容易受到潜在窃听者的攻击。该项目旨在开发新颖的IRS辅助DFRC系统设计，提供可靠的，高速率的信息，以预期的通信接收器，同时最大限度地减少窃听者访问的信息。安全DFRC系统的成功实施将在自动驾驶车辆、无人机、监视、搜索和救援行动以及涉及网络机器人的先进制造过程等应用中产生广泛的好处。该项目致力于最佳IRS辅助DFRC系统设计，包括两个重点。推力1考虑动态环境中的设计，例如城市通信环境。在这种情况下，动态优化系统参数的自然框架是深度强化学习（RL），因为它是自适应的，数据驱动的，不需要预先注释的数据。在Thrust 1中，将开发一个新的、有原则的深度RL框架，该框架将结合领域知识，并确保在梯度下降动态下的收敛。我们将考虑一种非策略的行动者-批评者方法，并研究批评者网络的神经切核（NTK）对深度RL算法的训练稳定性及其对看不见或很少经历的事件的性能的作用。智能窃听者将通过将性能优化问题制定为考虑合作和竞争的多代理强化学习问题来解决。推力2考虑了发送正交频分复用（OFDM）波形的DFRC系统，并引入了经由时间调制IRS的定向调制（TM-IRS）的新概念，其允许更灵活的安全系统设计。利用TM-IRS，IRS的每个元件跨OFDM符号周期性地开启/关闭。通过仔细设计周期性激活模式以及IRS参数和发射天线权重，信号可以在期望的方向上被完整地传递，同时在所有其他方向上看起来被加扰。TM-IRS为系统设计提供了大量的自由度，从而增强了DFRC系统的安全运行。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。