RINGS: WISECOM - Wireless Integrated Sensing, Learning and Communication Networks

未来的无线网络将集成传感和通信功能。传感能力可以来自网络中设备的传感器。无线电通信信号本身也可以用于传感，特别是在高载波频率下工作时，具有高带宽和大型天线阵列。例如，支持自动驾驶汽车或配备雷达、激光雷达或摄像头的工业机器人的蜂窝网络。该项目从硬件和软件的角度推进了基础技术，使综合传感、学习和通信（ISLAC）无线网络能够获取和传播与用户和网络操作本身有关的环境的准确信息。这些技术提供的传感精度至关重要，既可以支持给定的用例，又可以增强网络的弹性，从而对故障或错误配置做出快速响应。该项目的成果将通过提供更高的数据速率和更高的可靠性，以一种拥抱机器学习的方式改善人和设备的蜂窝连接，并在此类网络中部署丰富的传感器数据。为了建立综合传感、学习和通信网络的潜力，以增强其自身的复原力，该项目开发：(a)拉加经委会网络的核心使能技术，包括用于联合传感和通信的硬件和信号处理算法；(b)根据毫米波（mmWave）和次太赫兹（sub-THz）频段的特定传播特性和网络运行情况，测量弹性的数学工具；(c)利用感知信息提高网络适应性的学习策略；(d)以用户为中心的算法，利用感知信息来提高网络的自主性和增加。开发的策略将使用基于光线追踪、实验测量和混合数字、物理和虚拟世界的模型的组合的框架进行评估。该方法将能够在蜂窝网络支持的几个相关场景中评估已开发的技术，包括自动车辆、自动化工厂和沉浸式现实设置。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。

Future wireless networks will integrate sensing and communication functions. The sensing capabilities can come from the sensors of the devices in the network. The radio communication signal itself can also be used for sensing, especially when operating at high carrier frequencies, with high bandwidths and large antenna arrays. Examples are cellular networks supporting automated vehicles or industrial robots equipped with radar, lidar or cameras. This project advances the fundamental technologies, from a hardware and software perspective, to enable integrated sensing, learning and communication (ISLAC) wireless networks, capable of obtaining and communicating accurate information about the environment, relevant for the users and for the network operation itself. The sensing accuracy provided by these technologies is critical, both to support a given use case, and to enhance the resilience of the network, enabling a fast respond to failures or mis-configurations. The outcomes of this project will improve cellular connectivity for people and devices, by providing higher data rates, with more reliability, in a way that embraces machine learning and the wealth of sensor data also being deployed in such networks. To establish the potential of integrated sensing, learning and communication networks to enhance their own resilience, this project develops: (a) the core enabling technologies for ISLAC networks, including hardware and signal processing algorithms for joint sensing and communications; (b) mathematical tools to measure resilience accounting for the particular propagation features and network operation at millimeter wave (mmWave) and sub-Terahertz (sub-THz) bands; (c) learning strategies that exploit sensing information to improve network adaptability; and (d) user-centric algorithms that exploit sensing information to improve network autonomy and increase. The developed strategies will be evaluated using a framework based on a combination of ray tracing, experimental measurements and models that mix the digital, physical, and virtual worlds. This methodology will enable the evaluation of the developed technologies in several relevant scenarios supported by cellular networks, including automated vehicles, automated factories, and immersive reality settings.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.