As an important resource in data link,time slots should be strategically allocated to enhance transmission efficiency and resist eavesdropping,especially considering the tremendous increase in the number of nodes and ...As an important resource in data link,time slots should be strategically allocated to enhance transmission efficiency and resist eavesdropping,especially considering the tremendous increase in the number of nodes and diverse communication needs.It is crucial to design control sequences with robust randomness and conflict-freeness to properly address differentiated access control in data link.In this paper,we propose a hierarchical access control scheme based on control sequences to achieve high utilization of time slots and differentiated access control.A theoretical bound of the hierarchical control sequence set is derived to characterize the constraints on the parameters of the sequence set.Moreover,two classes of optimal hierarchical control sequence sets satisfying the theoretical bound are constructed,both of which enable the scheme to achieve maximum utilization of time slots.Compared with the fixed time slot allocation scheme,our scheme reduces the symbol error rate by up to 9%,which indicates a significant improvement in anti-interference and eavesdropping capabilities.展开更多
物联网(Internet of Things,IoT)与5G网络不断部署,边缘传感设备计算负载与持续供能需求显著增加。无线供能移动边缘计算(Wireless-Powered Mobile Edge Computing,WP-MEC)技术通过将无线能量传输(Wireless Power Transfer,WPT)与移动...物联网(Internet of Things,IoT)与5G网络不断部署,边缘传感设备计算负载与持续供能需求显著增加。无线供能移动边缘计算(Wireless-Powered Mobile Edge Computing,WP-MEC)技术通过将无线能量传输(Wireless Power Transfer,WPT)与移动边缘计算(Wireless Edge Computing,MEC)相结合,为延长边缘设备的供电寿命、提高系统整体计算能力提供了新途径。然而,前人的工作主要聚焦于单时隙资源优化或单小区网络模型,资源利用效率不高、与实际偏差较大。为此,聚焦基于非正交多址接入(Non-Orthogonal Multiple Access,NOMA)的多小区WP-MEC网络多时隙优化设计,通过联合优化能量传输时间、任务卸载策略与功率分配,充分利用能量累积增益,最大化系统长时吞吐量。为实现复杂动态环境下的高效资源调度,提出了一种异构多智能体近端策略优化算法(Heterogeneous Multi-Agent Proximal Policy Optimization,HMAPPO),采用全局控制智能体与设备智能体的分层结构,实现了全局能量传输时间与局部任务卸载、功率分配的协同优化。与基于价值函数更新网络的多智能体软演员-评论家(Multi-Agent Soft Actor-Critic,MASAC)和多智能体双延迟深度确定性策略梯度(Multi-Agent Twin Delayed Deep Deterministic Policy Gradient,MATD3)算法不同,HMAPPO采用了限制新旧策略变化幅度的近端策略优化更新机制,更适合多时隙能量的动态连续动作空间,因此在WP-MEC网络环境中具备更高的训练稳定性。仿真结果表明,所提HMAPPO算法在实现了分布式优化的情况下,达到了与集中式近端策略优化(Proximal Policy Optimization,PPO)相接近的性能,性能差别不到3.3%。此外,HMAPPO在不同小区数、设备数及设备距离条件下表现良好,验证了其优良的泛化性和可扩展性。展开更多
随着5G逐步向6G演进,通信感知一体化(Integrated Sensing and Communication,ISAC)技术成为满足未来无线通信和环境感知双重需求的关键路径,将广泛应用于智能家居、低空经济、车联网(Vehicle-to-Everything,V2X)等垂直行业。然而,在实...随着5G逐步向6G演进,通信感知一体化(Integrated Sensing and Communication,ISAC)技术成为满足未来无线通信和环境感知双重需求的关键路径,将广泛应用于智能家居、低空经济、车联网(Vehicle-to-Everything,V2X)等垂直行业。然而,在实际应用中,ISAC技术面临着复杂环境中的干扰抑制、频谱资源分配、信号处理和高动态场景下的适应性等多重挑战。阐述了ISAC在服务类场景、应用类场景等不同维度下的垂直行业应用分类以及典型用例,深入分析了ISAC在智能家居、无人机、V2X三种应用类场景中的性能要求、技术挑战和关键技术研究现状,如波形设计、干扰管理、资源分配等。总结现有技术并对未来ISAC在垂直行业所面临的开放性问题进行展望。展开更多
针对遮挡场景下车辆跟踪精度下降的问题,提出了一种基于卷积核优选的遮挡车辆跟踪(Convolutional Kernel Optimization for Occluded Vehicle Tracking,CKO-OVT)算法。CKO-OVT算法通过卷积核优选策略自适应挑选出对车辆目标更为敏感的...针对遮挡场景下车辆跟踪精度下降的问题,提出了一种基于卷积核优选的遮挡车辆跟踪(Convolutional Kernel Optimization for Occluded Vehicle Tracking,CKO-OVT)算法。CKO-OVT算法通过卷积核优选策略自适应挑选出对车辆目标更为敏感的卷积算子进行特征提取,通过判别式孪生网络对跟踪结果进行评估并在跟踪失效的情况下重定位目标,进一步提升跟踪的鲁棒性和准确性。实验部分,构建了遮挡车辆跟踪(Occluded Vehicle Tracking,OVT)数据集,分别在目标跟踪基准(Object Tracking Benchmark,OTB)数据集、TColor-128公开数据集和自建OVT数据集上同高效卷积跟踪(Efficient Convolution Operators for Tracking,ECO)算法、ECO轻量化版本(Efficient Convolution Operators for Tracking Using HOG and CN,ECOHC)、相关滤波(Kernelized Correlation Filters Tracker,KCF)算法、判别式尺度空间跟踪(Discriminative Scale Space Tracker,DSST)算法、循环结构核跟踪(Circulant Structure Kernel Tracker,CSK)算法、层次相关滤波跟踪(Hierarchical Convolutional Features for Visual Tracking,HCFT)算法、基于分层卷积特征的鲁棒视觉跟踪(Robust Visual Tracking via Hierarchical Convolutional Features,HCFTstar)算法、全卷积孪生网络跟踪(Fully-Convolutional Siamese Networks for Object Tracking,SiameseFC)算法和抗干扰感知孪生网络跟踪(Distractor-Aware Siamese Networks for Object Tracking,DaSiam)算法9种主流算法进行实验对比,实验结果表明CKO-OVT算法在OTB数据集上距离精确率提升了2.2%,重叠成功率提升了1.8%;在TColor-128数据集上距离精确率提升了0.4%,重叠成功率提升了0.9%;在OVT数据集上距离精确率提升了1.7%,重叠成功率提升了1.2%。CKO-OVT算法通过自适应卷积核优选和判别式孪生网络,显著提升了遮挡场景下车辆跟踪的鲁棒性和准确性,在OTB、TColor-128和自建OVT数据集上的实验结果表明,CKO-OVT算法在距离精确率和重叠成功率上优于主流跟踪算法,为智能交通和自动驾驶领域的车辆跟踪提供了有效的解决方案。展开更多
基金supported by the National Science Foundation of China(No.62171387)the Science and Technology Program of Sichuan Province(No.2024NSFSC0468)the China Postdoctoral Science Foundation(No.2019M663475).
文摘As an important resource in data link,time slots should be strategically allocated to enhance transmission efficiency and resist eavesdropping,especially considering the tremendous increase in the number of nodes and diverse communication needs.It is crucial to design control sequences with robust randomness and conflict-freeness to properly address differentiated access control in data link.In this paper,we propose a hierarchical access control scheme based on control sequences to achieve high utilization of time slots and differentiated access control.A theoretical bound of the hierarchical control sequence set is derived to characterize the constraints on the parameters of the sequence set.Moreover,two classes of optimal hierarchical control sequence sets satisfying the theoretical bound are constructed,both of which enable the scheme to achieve maximum utilization of time slots.Compared with the fixed time slot allocation scheme,our scheme reduces the symbol error rate by up to 9%,which indicates a significant improvement in anti-interference and eavesdropping capabilities.
文摘随着5G逐步向6G演进,通信感知一体化(Integrated Sensing and Communication,ISAC)技术成为满足未来无线通信和环境感知双重需求的关键路径,将广泛应用于智能家居、低空经济、车联网(Vehicle-to-Everything,V2X)等垂直行业。然而,在实际应用中,ISAC技术面临着复杂环境中的干扰抑制、频谱资源分配、信号处理和高动态场景下的适应性等多重挑战。阐述了ISAC在服务类场景、应用类场景等不同维度下的垂直行业应用分类以及典型用例,深入分析了ISAC在智能家居、无人机、V2X三种应用类场景中的性能要求、技术挑战和关键技术研究现状,如波形设计、干扰管理、资源分配等。总结现有技术并对未来ISAC在垂直行业所面临的开放性问题进行展望。
文摘针对遮挡场景下车辆跟踪精度下降的问题,提出了一种基于卷积核优选的遮挡车辆跟踪(Convolutional Kernel Optimization for Occluded Vehicle Tracking,CKO-OVT)算法。CKO-OVT算法通过卷积核优选策略自适应挑选出对车辆目标更为敏感的卷积算子进行特征提取,通过判别式孪生网络对跟踪结果进行评估并在跟踪失效的情况下重定位目标,进一步提升跟踪的鲁棒性和准确性。实验部分,构建了遮挡车辆跟踪(Occluded Vehicle Tracking,OVT)数据集,分别在目标跟踪基准(Object Tracking Benchmark,OTB)数据集、TColor-128公开数据集和自建OVT数据集上同高效卷积跟踪(Efficient Convolution Operators for Tracking,ECO)算法、ECO轻量化版本(Efficient Convolution Operators for Tracking Using HOG and CN,ECOHC)、相关滤波(Kernelized Correlation Filters Tracker,KCF)算法、判别式尺度空间跟踪(Discriminative Scale Space Tracker,DSST)算法、循环结构核跟踪(Circulant Structure Kernel Tracker,CSK)算法、层次相关滤波跟踪(Hierarchical Convolutional Features for Visual Tracking,HCFT)算法、基于分层卷积特征的鲁棒视觉跟踪(Robust Visual Tracking via Hierarchical Convolutional Features,HCFTstar)算法、全卷积孪生网络跟踪(Fully-Convolutional Siamese Networks for Object Tracking,SiameseFC)算法和抗干扰感知孪生网络跟踪(Distractor-Aware Siamese Networks for Object Tracking,DaSiam)算法9种主流算法进行实验对比,实验结果表明CKO-OVT算法在OTB数据集上距离精确率提升了2.2%,重叠成功率提升了1.8%;在TColor-128数据集上距离精确率提升了0.4%,重叠成功率提升了0.9%;在OVT数据集上距离精确率提升了1.7%,重叠成功率提升了1.2%。CKO-OVT算法通过自适应卷积核优选和判别式孪生网络,显著提升了遮挡场景下车辆跟踪的鲁棒性和准确性,在OTB、TColor-128和自建OVT数据集上的实验结果表明,CKO-OVT算法在距离精确率和重叠成功率上优于主流跟踪算法,为智能交通和自动驾驶领域的车辆跟踪提供了有效的解决方案。
