计算机科学
适应(眼睛)
无线
背景(考古学)
无线网络
信号(编程语言)
无线传感器网络
分布式计算
人机交互
电信
计算机网络
古生物学
物理
光学
生物
程序设计语言
作者
Nuria González‐Prelcic,Musa Furkan Keskin,Ossi Kaltiokallio,Mikko Valkama,Davide Dardari,Xiao Shen,Yuan Shen,Murat Bayraktar,Henk Wymeersch
标识
DOI:10.1109/jproc.2024.3397609
摘要
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.
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