Wireless communication is easily disturbed by unfortunate factors which drive the wireless environment unstable and complicated. Therefore, it is essential to consider these factors in stability analysis of the wireless network. However, wireless channel characteristics and packets collisions are neglected in the classical fluid model. A wireless TCP fluid model(WTFM) for stability analysis of wireless network is proposed based on cross layers, which not only makes the congestion control based on random early detection(RED) available for wireless network, but also provides a more accurate model to analyze the stability of wireless system theoretically. In the proposed model, active queue management, abnormality of wireless channels and packets collisions are taken into consideration. The comparisons between evaluating results from the WTFM and the practical performance from NS2 simulations validate the accuracy of the proposed WTFM in the perspectives of delay, dropping probability, throughput, sliding window size and queue length. A set of comparisons among the proposed WTFM, the classical fluid model and the convex optimization model are conducted. The results demonstrate that the proposed WTFM model performs better than other schemes in comprehensive aspects on capturing the characteristic of the wireless network and computing complexity.
随着现代社会的发展,各种智能终端已经成为生活中不可或缺的一部分,如何为其提供快速高效的网络服务是一个亟待解决的问题。使用移动融合网络,将固定网络与移动网络进行融合、充分发挥两者的优势,是当前研究的热点。首先介绍了移动融合网络的概念,然后提出了基于信道质量的分流算法,以解决移动融合网络中的分流问题。最后进行了仿真实验,分别仿真了数据全走长期演进(long term evolution,LTE)网络、数据全走无线保真(wireless fidelity,WiFi)网络、基于信道质量进行分流3种情况。通过对比发现该算法可以显著提高系统吞吐量、减小时延,从而验证了其有效性。
基于特征值分解(eigen value decomposition,EVD)或奇异值分解(singular value decomposition,SVD)的半盲信道估计算法需要进行重复的EVD或SVD计算,计算量较大,不适用于多小区多用户的大规模MIMO系统.为此,针对多小区多用户大规模MIMO系统中的半盲信道估计给出了一种快速实现算法.该算法主要利用最小二乘(least-squares)及线性最小均方误差(linear minimum mean-square error)原理导出一种新的递归计算模糊矩阵的方法,采用快速递归row-Householder子空间跟踪算法对接收向量的信号子空间的估计进行加速.仿真结果表明,所提出的算法估计性能良好,并能有效减轻导频污染的影响.
