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。感知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侦听，特别是在工作在高载波频率、高带宽和大天线阵列的情况下。例如支持自动化车辆或配备雷达、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基本技术，使综合传感、学习和通信(ISLAC)无线网络能够获得和交流与用户和网络运营本身有关的准确环境信息。这些技术提供的感知准确性对于支持给定用例和增强网络的弹性至关重要，从而能够对故障或错误配置做出快速响应。该项目的成果将改善人和设备的蜂窝连接，提供更高的数据速率和更可靠的方式，采用机器学习的方式，并将丰富的传感器数据也部署在此类网络中。为了确定综合传感、学习和通信网络提高自身复原力的潜力，该项目开发了：(A)综合传感、学习和通信网络的核心使能技术，包括联合传感和通信的硬件和信号处理算法；(B)考虑到毫米波(毫米波)和亚太赫兹(亚太赫兹)波段的特殊传播特征和网络运作的衡量复原力的数学工具；(C)利用传感信息提高网络适应性的学习战略；(D)利用传感信息改善网络自主性和增加的以用户为中心的算法。开发的策略将使用一个框架进行评估，该框架基于光线跟踪、实验测量和混合了数字、物理和虚拟世界的模型。这一方法将能够在蜂窝网络支持的几个相关场景中对开发的技术进行评估，包括自动化车辆、自动化工厂和身临其境的现实环境。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。

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.