GSM-R系统集成工程的项目管理经验

建设GSM-R铁路数字移动通信网络是一项非常复杂的系统工程.从涉及的网元来讲,有核心节点中的交换机、智能网、归属位置寄存器(HLR)和网管设备,无线设备中的基站、基站控制器、速率适配器、GPRS和用于隧道弱场的直放站、车载终端、手持终端以及网管设备.     

今天的网络孕育太比特的未来

太比特网络时代已为期不远,电信运营商的网络设施准备总超前于需求,他们已开始考虑当前网络建设和演进如何向未来太比特网络的平滑过渡。中国电信、Ｑｕｅｓｔ、Ｌｅｕｅｌ３Ｃｏｍｍｕｎｉｃａｔｉｏｎｓ等公司利用北电网络的方案和产品建设和优化了自己的网络。而光网络产品功能的不断演进和技术的突破正在为向未来端到端全光网演进清除障碍。     

interWAN分组传输：拓宽SDH的应用空间

北电网络的 ｉｎｔｅｒＷＡＮ分组传输一体化网络解决方案开创了一种经济有效的数据连接方式。本文对其进行说明,并对其可靠性、灵活性、可扩展性、可管理性及业务效益进行了论述。     

2011年2月份十大重要安全漏洞分析

2011年01月21日至2月20日,国家计算机网络入侵防范中心发布漏洞总条目为455条,漏洞总数比2011年1月增加了41.30%[1]。其中威胁级别为＂紧急＂的有157条,＂高＂的有68条,＂中＂的有199条,＂低＂的有31条。威胁级别为紧急和高的漏洞占到总量的49.45%,从漏洞利用方式来看,远程攻击的有389条,本地攻击的有64条,局域网攻击的有2条。可见,能够从远程进行攻击的漏洞占绝大多数,这使得攻击者利用相应漏洞发动攻击更为容易。本月微软发布12个安全公告,其中3个为严重等级,9个为重要等级,共修复Windows操作系统、IE浏览器等软件中的22个安全漏洞,受影响的操作系统为包括Windows7在内的多个Windows版本。其中,微软发布安全公告修复了InternetExplorer中的一个零日漏洞和Windows图像渲染引擎中的一个零日漏洞,以及潜伏期长达19年的Windows内核漏洞。这些零日漏洞已经被利用进行攻击。建议广大用户及时安装补丁,增强系统安全性,做好安全防范工作,保证信息系统安全。     

流量矩阵估计研究综述

随着Internet网络的快速发展,网络操作员迫切需要知道网络中数据的流动情况,以便更好地进行网络管理、网络监控、网络设计和网络规划等网络流量工程管理.流量矩阵作为网络流量工程的重要输入参数,已受到国内外研究人员的广泛研究,现已成为Internet的一个重要研究方向.本文介绍流量矩阵研究的现状,并从性能上分析和比较各种研究方法,最后讨论流量矩阵研究的技术难题,并指出新的发展方向.     

无线Mesh网络MAC性能的博弈分析及优化

将博弈论用于IEEE802．11无线Mesh网络媒体接入控制协议的性能分析和优化。通过将节点间的信道竞争过程建模为非完全信息动态博弈,求解博弈的纳什均衡,即各节点的最优分组发送概率,并据此提出改进的DCF协议（G-MAC）：各节点首先通过监测信道,对当前博弈状态（竞争信道的节点数）进行估计;然后根据估计到的博弈状态调整其均衡策略（最小竞争窗口）;最后通过有限次动态博弈获得最佳系统性能。同时,提出了一种能准确估计博弈状态的虚拟帧发送机制（VDCF）,使空闲节点在转换为发送状态时可快速调整到均衡策略。仿真研究表明：G-MAC协议可以显著提高无线Mesh网络的系统吞吐量,降低延迟、延迟抖动和丢帧率。     

Two-step transmission with artificial noise for secure wireless SIMO communications

The issue of physical layer security of the wireless single-input multiple-output(SIMO) system is addressed in this paper. We propose a two-step transmission scheme with artificial noise to improve the security and the scheme does not require the channel state information of the eavesdropper. In the first step, the destination which has multiple antennas generates a random reference symbol and artificial noise and transmits them in the direction of the source and its orthogonal space, respectively. The reference symbol is received by the source without being polluted by the artificial noise, while it is jammed by the artificial noise for the eavesdropper. In the second step, the symbol of the source is multiplied by the received reference symbol and then transmitted. Finally, the destination can directly estimate the symbol of the source using the reference symbol. However, the eavesdropper needs to additionally estimate the reference symbol for demodulating the symbol of the source, which usually obtains an inaccurate result due to the artificial noise. The informationtheoretic security of our scheme is analyzed and a lower bound of the achievable secrecy rate is derived. The performance of the new scheme is compared with that of the traditional approach under different input signals through numerical simulations. The results show that the proposed scheme outperforms the traditional SIMO approach especially in the high SNR region.     

采用自适应阵天线技术提高频谱使用率

介绍了自适应阵天线技术的实现原理，以及技术应用于移动通信系统中对频率使用率的改善。     

Diversity order of multiuser two-way relay networks with beamforming over Nakagami-m fading channels

The diversity performance of multiuser two-way relay networks is investigated over Nakagami-m fading channels.We consider an amplify-and-forward relay network with beamforming at the base station and all the mobile stations,and derive closed-form lower and upper bounds on the outage probabilities of two unidirectional links,respectively.Moreover,the exact expression for the diversity order and the asymptotic expressions for the outage probabilities of two unidirectional links are given too.Finally,simulatio...     

Estimation of convergence rate for multi-regression learning algorithm

In many applications, the pre-information on regression function is always unknown. Therefore, it is necessary to learn regression function by means of some valid tools. In this paper we investigate the regression problem in learning theory, i.e., convergence rate of regression learning algorithm with least square schemes in multi-dimensional polynomial space. Our main aim is to analyze the generalization error for multi-regression problems in learning theory. By using the famous Jackson operators in approximation theory, covering number, entropy number and relative probability inequalities, we obtain the estimates of upper and lower bounds for the convergence rate of learning algorithm. In particular, it is shown that for multi-variable smooth regression functions, the estimates are able to achieve almost optimal rate of convergence except for a logarithmic factor. Our results are significant for the research of convergence, stability and complexity of regression learning algorithm.