Future wireless networks will integrate sensing, learning, and communication to provide new services beyond communication and to become more resilient. Sensors at the network infrastructure, sensors on the user equipment (UE), and the sensing capability of the communication signal itself provide a new source of data that connects the physical and radio frequency (RF) environments. A wireless network that harnesses all these sensing data can not only enable additional sensing services but also become more resilient to channel-dependent effects such as blockage and better support adaptation in dynamic environments as networks reconfigure. In this article, we provide a vision for integrated sensing and communication (ISAC) networks and an overview of how signal processing, optimization, and machine learning (ML) techniques can be leveraged to make them a reality in the context of 6G. We also include some examples of the performance of several of these strategies when evaluated using a simulation framework based on a combination of ray-tracing measurements and mathematical models that mix the digital and physical worlds.

中文翻译：

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面向 6G 的集成传感和通信革命：视觉、技术和应用


未来的无线网络将集成传感、学习和通信，以提供通信之外的新服务，并变得更有弹性。网络基础设施中的传感器、用户设备 （UE） 上的传感器以及通信信号本身的传感功能提供了连接物理和射频 （RF） 环境的新数据源。利用所有这些传感数据的无线网络不仅可以实现额外的传感服务，还可以增强对信道依赖效应（如阻塞）的弹性，并在网络重新配置时更好地支持在动态环境中的适应。在本文中，我们提供了集成传感和通信 （ISAC） 网络的愿景，并概述了如何利用信号处理、优化和机器学习 （ML） 技术在 6G 环境中使其成为现实。我们还提供了一些示例，说明当使用基于光线追踪测量和混合数字世界和物理世界的数学模型组合的仿真框架进行评估时，其中几种策略的性能。