自动小区规划的实际应用考虑

利用自动小区规划(ACP)进行深度RF优化是近年来优化工作的一个研究热点。从ACP的理论基础和一般流程等方面深入剖析影响ACP输出优化方案准确性的主要因素。这些因素包括路测(DT)数据采集方法、区域模型的划分和待优化参数初始值及变化范围的设置。用网优实际案例验证我们分析的结果,为下一步优化实践工作提供了方向。     

一种基于多目标优化的神经网络快速学习算法

本文根据多目标优化理论、认知科学和神经科学等学科的最新成果，提出了一种基于多目标优化的神经网络快速学习算法．实验结果表明，该学习算法可以解决目前神经网络普遍存在的学习时间长、容易陷入局部极小和网络结构优化难等问题.     

On Reliability-optimized Controller Placement for Software-Defined Networks

By decoupling control plane and data plane, Software-Defined Networking （SDN） approach simplifies network management and speeds up network innovations. These benefits have led not only to prototypes, but also real SDN deployments. For wide-area SDN deployments, multiple controllers are often required, and the placement of these controllers becomes a particularly important task in the SDN context. This paper studies the problem of placing controllers in SDNs, so as to maximize the reliability of SDN control networks. We present a novel metric, called expected percentage of control path loss, to characterize the reliability of SDN control networks. We formulate the reliability-aware control placement problem, prove its NP-hardness, and examine several placement algorithms that can solve this problem. Through extensive simulations using real topologies, we show how the number of controllers and their placement influence the reliability of SDN control networks. Besides, we also found that, through strategic controller placement, the reliability of SDN control networks can be significantly improved without introducing unacceptable switch-to-controller latencies.