车联网借助新一代信息通信技术,实现人、车、路、云等的互联互通.未来beyond 5G(B5G)和6G将赋予下一代车联网更极致的通信与感知性能,有效支撑智能驾驶与智慧交通等创新应用.然而,车辆高速移动带来的高多普勒效应,极大地增加了现有正交...车联网借助新一代信息通信技术,实现人、车、路、云等的互联互通.未来beyond 5G(B5G)和6G将赋予下一代车联网更极致的通信与感知性能,有效支撑智能驾驶与智慧交通等创新应用.然而,车辆高速移动带来的高多普勒效应,极大地增加了现有正交频分复用(Orthogonal frequency division multiplexing,OFDM)系统的载波间干扰和导频开销,尤其是B5G/6G时代毫米波、太赫兹等高频段的广泛应用将进一步加剧这一问题.近年来,正交时频空间(Orthogonal time frequency space, OTFS)技术由于在抗时频双域选择性衰落方面的显著优势受到了业界的广泛关注.基于OTFS实现通信与感知一体化成为了车联网领域的研究热点.本文旨在研究基于OTFS的车联网通感一体化的系统原理、关键技术、应用模式及技术挑战.首先,在现有OTFS通信系统的基础上,探讨OTFS通感一体化的系统架构、实现原理以及通信和感知性能.然后,介绍OTFS技术的国内外研究现状,并进一步从物理层帧结构、导频机制等方面讨论OTFS通感一体化的难点与关键技术.最后,结合实际场景,分析OTFS在车联网通感一体化中的应用及面临的主要挑战.展开更多
The new applications surge with the rapid evolution of the mobile communications.The explosive growth of the data traffic aroused by the new applications has posed great computing pressure on the local side.It is esse...The new applications surge with the rapid evolution of the mobile communications.The explosive growth of the data traffic aroused by the new applications has posed great computing pressure on the local side.It is essential to innovate the computation offloading methods to alleviate the local computing burden and improve the offloading efficiency.Mobile edge computing(MEC)assisted by reflecting intelligent surfaces(RIS)-based unmanned aerial vehicle(UAV)is a promising method to assist the users in executing the computation tasks in proximity at low cost.In this paper,we propose an energy-efficient MEC system assisted by RIS-based UAV,where the UAV with RIS mounted relays the computation tasks to the MEC server.The energy efficiency maximization problem is formulated by jointly optimizing the UAV's trajectory,the transmission power of all users,and the phase shifts of the reflecting elements placed on the UAV.Considering that the optimization problem is non-convex,we propose a deep deterministic policy gradient(DDPG)-based algorithm.By combining the DDPG algorithm with the energy efficiency maximization problem,the optimization problem can be resolved.Finally,the numerical results are illustrated to show the performance of the system and the superiority compared with the benchmark schemes.展开更多
文摘车联网借助新一代信息通信技术,实现人、车、路、云等的互联互通.未来beyond 5G(B5G)和6G将赋予下一代车联网更极致的通信与感知性能,有效支撑智能驾驶与智慧交通等创新应用.然而,车辆高速移动带来的高多普勒效应,极大地增加了现有正交频分复用(Orthogonal frequency division multiplexing,OFDM)系统的载波间干扰和导频开销,尤其是B5G/6G时代毫米波、太赫兹等高频段的广泛应用将进一步加剧这一问题.近年来,正交时频空间(Orthogonal time frequency space, OTFS)技术由于在抗时频双域选择性衰落方面的显著优势受到了业界的广泛关注.基于OTFS实现通信与感知一体化成为了车联网领域的研究热点.本文旨在研究基于OTFS的车联网通感一体化的系统原理、关键技术、应用模式及技术挑战.首先,在现有OTFS通信系统的基础上,探讨OTFS通感一体化的系统架构、实现原理以及通信和感知性能.然后,介绍OTFS技术的国内外研究现状,并进一步从物理层帧结构、导频机制等方面讨论OTFS通感一体化的难点与关键技术.最后,结合实际场景,分析OTFS在车联网通感一体化中的应用及面临的主要挑战.
基金supported by National Key Research and Development Program of China(2021YFB2900801)supported by Guangdong Basic and Applied Basic Research Foundation(2022A1515110335)+2 种基金supported by Fundamental Research Funds for the Central Universities(FRF-TP-22-094A1)supported by Science,Technology and Innovation Project of Xiongan New Area(2022XAGG0114)supported by Meteorological information and Signal Processing Key Laboratory of Sichuan Higher Education Institutes of Chengdu University of Information Technology(CXHCL202201).
文摘The new applications surge with the rapid evolution of the mobile communications.The explosive growth of the data traffic aroused by the new applications has posed great computing pressure on the local side.It is essential to innovate the computation offloading methods to alleviate the local computing burden and improve the offloading efficiency.Mobile edge computing(MEC)assisted by reflecting intelligent surfaces(RIS)-based unmanned aerial vehicle(UAV)is a promising method to assist the users in executing the computation tasks in proximity at low cost.In this paper,we propose an energy-efficient MEC system assisted by RIS-based UAV,where the UAV with RIS mounted relays the computation tasks to the MEC server.The energy efficiency maximization problem is formulated by jointly optimizing the UAV's trajectory,the transmission power of all users,and the phase shifts of the reflecting elements placed on the UAV.Considering that the optimization problem is non-convex,we propose a deep deterministic policy gradient(DDPG)-based algorithm.By combining the DDPG algorithm with the energy efficiency maximization problem,the optimization problem can be resolved.Finally,the numerical results are illustrated to show the performance of the system and the superiority compared with the benchmark schemes.
