为提高零中频接收机中正交(in-phase quadrature,IQ)失配信号校正的收敛速度与鲁棒性,本文将Kalman滤波算法与盲源分离结构结合,提出了一种基于双通道Kalman滤波的校正算法。该算法通过状态空间建模与协方差自适应更新,能够在动态环境...为提高零中频接收机中正交(in-phase quadrature,IQ)失配信号校正的收敛速度与鲁棒性,本文将Kalman滤波算法与盲源分离结构结合,提出了一种基于双通道Kalman滤波的校正算法。该算法通过状态空间建模与协方差自适应更新,能够在动态环境下实现更高效、稳定的参数估计,从而实现对IQ失配信号的有效补偿。将本文算法与最小均方算法(least mean square,LMS)、归一化最小均方算法(normalized least mean square,NLMS)和仿射投影算法(affine projection algorithm,APA)进行对比仿真,结果显示,校正后信号的镜像抑制比(image rejection ratio,IRR)均达到约45 dB,但双通道Kalman滤波算法对应的IRR曲面图更加平滑,同时,16QAM和16PSK调制方式下该算法的误符号率最低,表明本文算法能够有效实现IQ失配校正,具有较好的稳定性。本文算法迭代约50次时,均方误差收敛趋近于0,而LMS、NLMS和APA算法则分别需要迭代约500次、400次和200次才能够收敛,表明该算法具有较好的收敛性。通过参数的敏感性仿真分析,在较大的参数范围内本文算法达到的IRR差别甚微,具有良好的鲁棒性。展开更多
正交时频空(Orthogonal Time Frequency Space,OTFS)调制因其在高多普勒频偏环境下的可靠传输能力,已成为低轨卫星等高动态通信场景的关键技术。然而,作为多载波调制技术,OTFS信号的高峰均功率比(Peak-to-Average Power Ratio,PAPR)易...正交时频空(Orthogonal Time Frequency Space,OTFS)调制因其在高多普勒频偏环境下的可靠传输能力,已成为低轨卫星等高动态通信场景的关键技术。然而,作为多载波调制技术,OTFS信号的高峰均功率比(Peak-to-Average Power Ratio,PAPR)易导致功放进入非线性工作状态,产生信号失真,影响通信可靠性和稳定性。格雷互补序列因其特殊的定义,使得该序列的最大峰均比不超过3 dB。基于里德-穆勒(Reed-Muller,RM)编码与格雷互补序列之间的特殊联系,提出了一种基于RM编码的OTFS系统的峰均功率比抑制方法。在发射端,首先利用RM编码将原始比特流序列编码转换为格雷互补序列形式,再进行星座映射与OTFS调制,得到低峰均功率比的发射信号。在接收端,为了实现对这种特殊编码信号的准确译码,设计了一种两步级联译码算法,通过陪集选择译码与单项式系数译码的级联实现了对具有格雷互补序列的RM编码的纠错译码,保证了通信传输的可靠性。仿真结果表明,在低轨卫星通信场景下,该编码方法可以将OTFS系统发射信号的峰均功率比抑制在3 dB以内;相较于OFDM系统,OTFS系统具有更强的鲁棒性;两步级联译码算法实现了较高信噪比(>6 dB)下更高的传输可靠性。上述方案的提出不仅为OTFS调制技术在星地高动态通信场景中的应用提供了有力的技术支持,也为未来多载波调制信号的峰均比抑制提供了新的参考。展开更多
随着第六代移动通信系统(6th generation mobile communication system, 6G)通信技术的发展,空天地一体化网络(Spaceair-ground integrated network, SAGIN)作为6G的重要组成部分,旨在实现卫星、空中平台与地面系统的无缝互联,在应急通...随着第六代移动通信系统(6th generation mobile communication system, 6G)通信技术的发展,空天地一体化网络(Spaceair-ground integrated network, SAGIN)作为6G的重要组成部分,旨在实现卫星、空中平台与地面系统的无缝互联,在应急通信、环境监测、智能交通等领域展现出巨大的潜力.然而,SAGIN具有异构结构、链路动态性高、资源分布广泛等特征,给网络的高效管理与优化带来巨大的挑战.近年来,人工智能(Artificial intelligence, AI)技术凭借强大的感知、学习与自主决策能力应用于通信网络,为SAGIN的智能演进提供了新契机.本文首先系统介绍SAGIN网络架构的基本组成与关键特征,并梳理当前主流AI技术在网络优化中的主要技术体系与适配优势,包括机器学习、图神经网络以及强化学习.其次,本文深入探讨了AI技术在SAGIN中智能资源管理、移动性管理与路由优化、空中平台路径规划、任务卸载与计算协同等典型场景中的应用与最新进展.最后,本文总结了AI技术应用在SAGIN网络中面临的挑战并展望了AI与SAGIN融合发展的未来方向.本文概述了AI技术在SAGIN网络中应用的优势与进展,旨在为AI赋能的SAGIN研究与应用发展提供技术参考.展开更多
Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrain...Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit(RSU),thereby achieving lower delay and energy consumption.However,due to the limited storage capacity and energy budget of RSUs,it is challenging to meet the demands of the highly dynamic Internet of Vehicles(IoV)environment.Therefore,determining reasonable service caching and computation offloading strategies is crucial.To address this,this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading.By modeling the dynamic optimization problem using Markov Decision Processes(MDP),the scheme jointly optimizes task delay,energy consumption,load balancing,and privacy entropy to achieve better quality of service.Additionally,a dynamic adaptive multi-objective deep reinforcement learning algorithm is proposed.Each Double Deep Q-Network(DDQN)agent obtains rewards for different objectives based on distinct reward functions and dynamically updates the objective weights by learning the value changes between objectives using Radial Basis Function Networks(RBFN),thereby efficiently approximating the Pareto-optimal decisions for multiple objectives.Extensive experiments demonstrate that the proposed algorithm can better coordinate the three-tier computing resources of cloud,edge,and vehicles.Compared to existing algorithms,the proposed method reduces task delay and energy consumption by 10.64%and 5.1%,respectively.展开更多
文摘为提高零中频接收机中正交(in-phase quadrature,IQ)失配信号校正的收敛速度与鲁棒性,本文将Kalman滤波算法与盲源分离结构结合,提出了一种基于双通道Kalman滤波的校正算法。该算法通过状态空间建模与协方差自适应更新,能够在动态环境下实现更高效、稳定的参数估计,从而实现对IQ失配信号的有效补偿。将本文算法与最小均方算法(least mean square,LMS)、归一化最小均方算法(normalized least mean square,NLMS)和仿射投影算法(affine projection algorithm,APA)进行对比仿真,结果显示,校正后信号的镜像抑制比(image rejection ratio,IRR)均达到约45 dB,但双通道Kalman滤波算法对应的IRR曲面图更加平滑,同时,16QAM和16PSK调制方式下该算法的误符号率最低,表明本文算法能够有效实现IQ失配校正,具有较好的稳定性。本文算法迭代约50次时,均方误差收敛趋近于0,而LMS、NLMS和APA算法则分别需要迭代约500次、400次和200次才能够收敛,表明该算法具有较好的收敛性。通过参数的敏感性仿真分析,在较大的参数范围内本文算法达到的IRR差别甚微,具有良好的鲁棒性。
文摘正交时频空(Orthogonal Time Frequency Space,OTFS)调制因其在高多普勒频偏环境下的可靠传输能力,已成为低轨卫星等高动态通信场景的关键技术。然而,作为多载波调制技术,OTFS信号的高峰均功率比(Peak-to-Average Power Ratio,PAPR)易导致功放进入非线性工作状态,产生信号失真,影响通信可靠性和稳定性。格雷互补序列因其特殊的定义,使得该序列的最大峰均比不超过3 dB。基于里德-穆勒(Reed-Muller,RM)编码与格雷互补序列之间的特殊联系,提出了一种基于RM编码的OTFS系统的峰均功率比抑制方法。在发射端,首先利用RM编码将原始比特流序列编码转换为格雷互补序列形式,再进行星座映射与OTFS调制,得到低峰均功率比的发射信号。在接收端,为了实现对这种特殊编码信号的准确译码,设计了一种两步级联译码算法,通过陪集选择译码与单项式系数译码的级联实现了对具有格雷互补序列的RM编码的纠错译码,保证了通信传输的可靠性。仿真结果表明,在低轨卫星通信场景下,该编码方法可以将OTFS系统发射信号的峰均功率比抑制在3 dB以内;相较于OFDM系统,OTFS系统具有更强的鲁棒性;两步级联译码算法实现了较高信噪比(>6 dB)下更高的传输可靠性。上述方案的提出不仅为OTFS调制技术在星地高动态通信场景中的应用提供了有力的技术支持,也为未来多载波调制信号的峰均比抑制提供了新的参考。
基金supported by Key Science and Technology Program of Henan Province,China(Grant Nos.242102210147,242102210027)Fujian Province Young and Middle aged Teacher Education Research Project(Science and Technology Category)(No.JZ240101)(Corresponding author:Dong Yuan).
文摘Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit(RSU),thereby achieving lower delay and energy consumption.However,due to the limited storage capacity and energy budget of RSUs,it is challenging to meet the demands of the highly dynamic Internet of Vehicles(IoV)environment.Therefore,determining reasonable service caching and computation offloading strategies is crucial.To address this,this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading.By modeling the dynamic optimization problem using Markov Decision Processes(MDP),the scheme jointly optimizes task delay,energy consumption,load balancing,and privacy entropy to achieve better quality of service.Additionally,a dynamic adaptive multi-objective deep reinforcement learning algorithm is proposed.Each Double Deep Q-Network(DDQN)agent obtains rewards for different objectives based on distinct reward functions and dynamically updates the objective weights by learning the value changes between objectives using Radial Basis Function Networks(RBFN),thereby efficiently approximating the Pareto-optimal decisions for multiple objectives.Extensive experiments demonstrate that the proposed algorithm can better coordinate the three-tier computing resources of cloud,edge,and vehicles.Compared to existing algorithms,the proposed method reduces task delay and energy consumption by 10.64%and 5.1%,respectively.
