以用户为中心的可见光通信协作传输中训练资源分配算法

以用户为中心的可见光通信协作传输是近年来出现的新架构,这导致虚拟小区之间出现重叠。为避免导频污染问题,每个虚拟小区中的光接入点(AP)或者虚拟小区中选择相同AP的用户发送的训练序列应该是正交的。针对可见光通信中以用户为中心的协作网络,研究训练资源的正交分配问题,提出了一种新的导频分配算法,联合导频分配和用户选择问题,以期最大限度地增加虚拟小区内可被接入的用户数。分析和仿真结果表明,该导频分配方案可以有效改善导频污染问题,提高训练资源利用率,并且相比已有的导频分配方案,性能有所改进。     

内插法在相关干涉仪测向体制中的应用

测向误差的减小一直以来都是无线电测向精度提高的一个难点,对于不同的测向体制所采用的减小测向误差的方法都不尽相同。本文主要介绍的是相关干涉仪测向体制的基本原理以及通过内插法的应用来减小测向误差的方法。     

Distributed learning consensus control based on neural networks for heterogeneous nonlinear multiagent systems

This paper considers a novel distributed iterative learning consensus control algorithm based on neural networks for the control of heterogeneous nonlinear multiagent systems. The system's unknown nonlinear function is approximated by suitable neural networks; the approximation error is countered by a robust term in the control. Two types of control algorithms, both of which utilize distributed learning laws, are provided to achieve consensus. In the provided control algorithms, the desired reference is considered to be an unknown factor and then estimated using the associated learning laws. The consensus convergence is proven by the composite energy function method. A numerical simulation is ultimately presented to demonstrate the efficacy of the proposed control schemes.