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宽带短波通信系统作为中远程主要通信手段受到了人们的广泛关注,研究宽带短波系统的重点和难点是宽带短波的信道建模。Watterson信道模型作为比较经典的窄带模型,从它的基础上衍生出2种宽带短波信道模型,Watterson后接高斯随机延迟模型和Watterson后接群延迟特性滤波器模型。另外还介绍了电离层物理模型、Volger决定性模型、ITS信道模型及其改进、基于并行子路径结构的宽带信道模型、伪决定性信道模型和子带并行-宽带窄带化模型。对这几种模型的建模思想以及基本原理进行了叙述,对其复杂度进行了比较并且对可行性进行了分析。 相似文献
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本文针对无人驾驶的动力学建模和数据处理问题,从汽车上坡时的动力学分析出发,综合考虑各种参数,研究建立了乘用车上坡过程的数学模型和处理建模数据的方法。在此基础上,建立乘用车上坡过程的机器学习网络,利用BP神经网络对动力学建模数据进行训练,使得建模的误差大幅减少。仿真结果表明,动力学模型得到了优化,实现了乘用车上坡过程的智能化参数分配。 相似文献
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无线信道估计是部署可重构智能超表面(Reconfigurable Intelligent Surface, RIS)辅助通信系统的关键与前提,然而下行链路传输环境下信道估计困难且导频开销大是对智能超表面辅助通信的重大挑战。针对以上问题,提出了一种基于分布式机器学习(Distributed Machine Learning, DML)训练模型的区域交集切换方案。首先,建立了一个多用户共享的下行信道估计神经网络,通过DML技术协同用户与基站训练网络模型。其次,搭建分层次神经网络结构对用户区域信道进行分类和特征提取。最后,针对用户处于相邻信道交集位置问题采用特征区域模型融合。仿真结果表明,基于区域交集的DML模型方案能在减少信道训练导频开销的同时最大化信道估计的精准性能。 相似文献
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针对遥感图像场景分类任务中训练样本数量少及遥感图像背景复杂等问题,本文将迁移学习和通道注意力引入到卷积神经网络(convolutional neural network,CNN) 中,提出基于迁移学习和通道注意力的遥感图像场景分类方法。该方法首先选用经过ImageNet自然数据集预训练的两个CNN作为主干,同时引入通道注意力机制,自适应地增强主要特征,抑制次要特征;然后融合这两个网络提取的特征进行分类;最后采用微调迁移学习的方式实现目标域上的学习与分类。提出的方法在几个经典的公共数据集上进行了评估,实验结果证明了本文提出的方法在遥感图像场景分类中达到与其他先进方法相当的性能。 相似文献
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在基于误差最小化的极限学习机(EM_ELM)的基础上,提出了一种改进的基于误差最小化的极限学习机,输入权重和偏置采用递归最小二乘法获得.实验证明,该方法具有更快的学习速度、良好的预测精度和更精简的网络结构. 相似文献
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基于39 GHz室外微蜂窝场景实测数据,开展了毫米波段路径损耗、阴影衰落和大尺度参数的建模与仿真研究.介绍了毫米波段喇叭旋转测量系统下空间交替广义期望最大化(Space-Alternating Generalized Expectation-maximization,SAGE)算法信号模型,优化的分簇算法与莱斯因子计算方法.基于SAGE提取多径参数,利用优化的分簇算法提取并分析了簇参数,包括簇内角度扩展、簇内时延扩展以及簇的数目,并根据测量结果验证了第三代合作伙伴计划(The 3rd Generation Partnership Project,3GPP)第五代(the 5th Generation,5G)移动通信标准推荐的仿真平台准确定性无线信道产生器(Quasi-Deterministic Radio Channel Generator,QuaDRiGa)在39 GHz的可用性.结果表明:在视距径下,方向性路损和全向路损在固定截距和浮动截距两种拟合方式下与自由空间路损模型接近;大尺度参数统计特性与基于毫米波的第五代集成通信移动无线电接入网络(Millimetre-Wave Based Mobile Radio Access Network for Fifth Generation Integrated Communications,mmMAGIC)、3GPP结论接近;视距径与非视距径的簇参数差别较小,且簇的个数较6 GHz下的频段更少.本文为5G毫米波39 GHz频段信道仿真和系统设计提供了重要的信道模型和参数. 相似文献
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针对高速移动正交频分复用系统,提出了一种新型的基于深度学习的时变信道预测方法。为了避免网络参数随机初始化造成的影响,本文方法首先基于数据与导频信息获取较理想的信道估计,利用其对BP神经网络进行预训练处理,以获取理想的网络初始参数;然后,基于预训练获取网络初始值,利用基于导频获取的信道估计对BP神经网络进行再次训练,以获取最终的信道预测网络模型;最后,本文方法基于该预测网络模型通过线上预测实现了时变信道的单时刻与多时刻预测。仿真结果表明,本文方法可以显著地提高时变信道预测精度,且具有较低的计算复杂度。 相似文献
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Wireless Mesh Networks (WMN) with multiple radios and multiple channels are expected to resolve the capacity limitation problem of simpler wireless networks. However, optimal WMN channel assignment (CA) is NP complete, and it requires an optimal mapping of available channels to interfaces mounted over mesh routers. Acceptable solutions to CA must minimize network interference and maximize available network throughput. In this paper, we propose a CA solution called as cluster‐based channel assignment (CBCA). CBCA aims at minimizing co‐channel interference yet retaining topology through non‐default CA. Topology preservation is important because it avoids network partitions and is compatible with single‐interface routers in the network. A ‘non‐default’ CA solution is desired because it uses interfaces over different channels and reduces medium contention among neighbors. To the best of our knowledge, CBCA is a unique cluster‐based CA algorithm that addresses topology preservation using a non‐default channel approach. The main advantage of CBCA is it runs in a distributed manner by allowing cluster heads to perform CA independently. CBCA runs in three stages, where first the WMN nodes are partitioned into clusters. The second stage performs binding of interfaces to neighbors and third stage performs CA. The proposed algorithm improves over previous work because it retains network topology and minimizes network interference, which in turn improves available network throughput. Further, when compared with two other CBCA algorithms, CBCA provides better performance in terms of improved network interference, throughput, delay, and packet delivery ratios when tested upon network topologies with various network densities and traffic loads. Copyright © 2014 John Wiley & Sons, Ltd. 相似文献
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Brian Russell Michael L. Littman Wade Trappe 《International Journal of Communication Systems》2011,24(7):950-966
The nodes in a wireless ad hoc network act as routers in a self‐configuring network without infrastructure. An application running on the nodes in the ad hoc network may require that intermediate nodes act as routers, receiving and forwarding data packets to other nodes to overcome the limitations of noise, router congestion and limited transmission power. In existing routing protocols, the ‘self‐configuring’ aspects of network construction have generally been limited to the construction of routes that minimize the number of intermediate nodes on a route while ignoring the effects that the resulting traffic has on the overall communication capacity of the network. This paper presents a context‐aware routing metric that factors the effects of environmental noise and router congestion into a single time‐based metric, and further presents a new cross‐layer routing protocol, called Warp‐5 (Wireless Adaptive Routing Protocol, Version 5), that uses the new metric to make better routing decisions in heterogeneous network systems. Simulation results for Warp‐5 are presented and compared to the existing, well‐known AODV (Ad hoc On‐Demand Distance Vector) routing protocol and the reinforcement‐learning based routing protocol, Q‐routing. The results show Warp‐5 to be superior to shortest path routing protocols and Q‐routing for preventing router congestion and packet loss due to noise. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献