Suppr超能文献

6G网络中NF-ISAC的路线图:全面概述与教程

A Roadmap for NF-ISAC in 6G: A Comprehensive Overview and Tutorial.

作者信息

Hakimi Azar, Galappaththige Diluka, Tellambura Chintha

机构信息

Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada.

出版信息

Entropy (Basel). 2024 Sep 10;26(9):773. doi: 10.3390/e26090773.

Abstract

Near-field (NF) integrated sensing and communication (ISAC) has the potential to revolutionize future wireless networks. It enables simultaneous communication and sensing operations on the same radio frequency (RF) resources using a shared hardware platform, maximizing resource utilization. NF-ISAC systems can improve communication and sensing performance compared to traditional far-field (FF) ISAC systems by exploiting the unique propagation characteristics of NF spherical waves with an additional distance dimension. Despite its potential, NF-ISAC research is still in its early stages, and a comprehensive survey of the technology is lacking. This paper systematically explores NF-ISAC technology, providing an in-depth analysis of both NF and FF systems, their applicability in various scenarios, and different channel models. It highlights the advantages and philosophies of ISAC, examining both narrow-band and wide-band NF-ISAC systems. Case studies and simulations offer deeper insights into NF-ISAC design philosophies. Additionally, the paper reviews the existing NF-ISAC literature, methodologies, potentials, and conclusions, and discusses future research areas, challenges, and applications.

摘要

近场(NF)集成传感与通信(ISAC)有潜力彻底改变未来无线网络。它能够使用共享硬件平台在相同射频(RF)资源上同时进行通信和传感操作,从而最大限度地提高资源利用率。与传统远场(FF)ISAC系统相比,NF-ISAC系统通过利用具有额外距离维度的NF球面波的独特传播特性,可以提高通信和传感性能。尽管具有潜力,但NF-ISAC研究仍处于早期阶段,且缺乏对该技术的全面综述。本文系统地探索了NF-ISAC技术,对NF和FF系统、它们在各种场景中的适用性以及不同的信道模型进行了深入分析。它突出了ISAC的优势和理念,研究了窄带和宽带NF-ISAC系统。案例研究和仿真为NF-ISAC设计理念提供了更深入的见解。此外,本文回顾了现有的NF-ISAC文献、方法、潜力和结论,并讨论了未来的研究领域、挑战和应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b76d/11431028/da462582880b/entropy-26-00773-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验