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

随着下一代无线系统努力将感知功能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。一个有希望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实现同时侦听和通信，从而提高频谱利用率、硬件效率和节能。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，可以采用一种名为智能反射表面(IRS)的新兴技术。IRS涉及许多低成本、实时可配置元件的平面阵列，这些元件智能地处理传输的波形，创建智能传播环境。虽然DFRC系统在感知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，因此它们很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，在将窃听者可访问的信息最小化的同时，向预期的通信接收器提供可靠的、高速率的信息。安全DFRC系统的成功实施将在自动驾驶车辆、无人机、监视、搜救行动和涉及网络机器人的先进制造工艺等应用中产生广泛的好处。该项目解决了IRS辅助的DFRC系统的优化设计，包括两个方面。推力1考虑在动态环境中进行设计，例如城市通信环境。在这种情况下动态优化系统参数的一个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，并且不需要预先注释的数据。在推力1中，将开发一个新颖且有原则的深度RL框架，该框架整合了领域知识，并确保在梯度下降动态下的收敛。将考虑一种非策略参与者-批评者方法，并将调查批评者网络的神经切线核(NTK)对深度RL算法的训练稳定性的作用及其对未知或很少经历的事件的性能。智能窃听者将通过将性能优化问题描述为一个同时考虑合作和竞争的多智能体强化学习问题来解决。推力2考虑了DFRC系统传输的正交频分复用(Ofdm)波形，并引入了通过时间调制的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。利用TM-IRS，IRS的每个元素周期性地跨ofdm符号开启/关闭。通过仔细设计周期性激活模式以及IRS参数和发射天线重量，信号可以在所需方向上完好无损地传输，而在所有其他方向上看起来都是加扰的。TM-IRS为系统设计提供了大量自由度，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。

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.