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

随着下一代无线系统努力将传感能力与通信功能结合在一起，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。一种有前途的解决方案是利用双功能雷达通信（DFRC）系统。这些系统利用单个设备和单个波形来实现同步传感和通信，从而提高频谱利用率，提高硬件效率并节省电力。为了进一步提高DFRC系统在具有挑战性的信道条件下的性能，可以采用一种称为智能反射表面（IRS）的新兴技术。IRS包括一个由众多低成本、实时可配置元素组成的平面阵列，这些元素可以智能地操纵传输波形，从而创建一个智能传播环境。虽然DFRC系统在传感和通信方面都具有很高的性能，但由于探测波形中嵌入了通信信息，因此容易受到潜在窃听者的攻击。该项目旨在开发新的irs辅助DFRC系统设计，向预期的通信接收器提供可靠、高速率的信息，同时最大限度地减少窃听者可以访问的信息。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。该项目解决了irs辅助DFRC系统的最佳设计，并包含两个重点。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。在推力1中，将开发一种新的原则性深度强化学习框架，该框架将结合领域知识并确保梯度下降动态下的收敛性。本文将考虑一种非政策行为者-批评家方法，并研究评论家网络的神经切线核（NTK）对深度强化学习算法的训练稳定性及其对未见或很少经历的事件的表现的作用。智能窃听将通过将性能优化问题表述为考虑合作和竞争的多智能体强化学习问题来解决。Thrust 2考虑了DFRC系统传输正交频分复用（OFDM）波形，并引入了通过时调IRS （TM-IRS）定向调制的新概念，从而允许更灵活的安全系统设计。使用TM-IRS， IRS的每个元件周期性地在OFDM符号上开/关。通过精心设计周期激活模式以及IRS参数和发射天线权重，可以使信号在期望的方向上完好无损地传输，而在所有其他方向上都出现乱象。TM-IRS为系统设计提供了大量的自由度，从而增强了DFRC系统运行的安全性。该奖项反映了美国国家科学基金会的法定使命，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。

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.