For Future networks, many research projects have proposed different architectures around the globe;Software Defined Network(SDN) architectures, through separating Data and Control Layers, offer a crucial structure for...For Future networks, many research projects have proposed different architectures around the globe;Software Defined Network(SDN) architectures, through separating Data and Control Layers, offer a crucial structure for it. With a worldwide view and centralized Control, the SDN network provides flexible and reliable network management that improves network throughput and increases link utilization. In addition, it supports an innovative flow scheduling system to help advance Traffic Engineering(TE). For Medium and large-scale networks migrating directly from a legacy network to an SDN Network seems more complicated & even impossible, as there are High potential challenges, including technical, financial, security, shortage of standards, and quality of service degradation challenges. These challenges cause the birth and pave the ground for Hybrid SDN networks, where SDN devices coexist with traditional network devices. This study explores a Hybrid SDN network’s Traffic Engineering and Quality of Services Issues. Quality of service is described by network characteristics such as latency, jitter, loss, bandwidth,and network link utilization, using industry standards and mechanisms in a Hybrid SDN Network. We have organized the related studies in a way that the Quality of Service may gain the most benefit from the concept of Hybrid SDN networks using different algorithms and mechanisms: Deep Reinforcement Learning(DRL), Heuristic algorithm, K path partition algorithm, Genetic algorithm, SOTE algorithm, ROAR method, and Routing Optimization with different optimization mechanisms that help to ensure high-quality performance in a Hybrid SDN Network.展开更多
为解决当前5G无线网络中高速移动节点信号定位不准确和高稳定分布噪声(high stable distribution noise,HSDN)等特殊偏移噪声降低定位准确性的问题,提出基于电控旋转序列位移接收结构的5G无线网络信号定位算法。利用单一节点接收结构及...为解决当前5G无线网络中高速移动节点信号定位不准确和高稳定分布噪声(high stable distribution noise,HSDN)等特殊偏移噪声降低定位准确性的问题,提出基于电控旋转序列位移接收结构的5G无线网络信号定位算法。利用单一节点接收结构及环接收结构并采取分层方式,构建电控旋转序列位移接收结构;基于待定位信号与中央基站及各层接收节点之间的切线关系,初步估计待测信号方位;针对信号发射环境中的HSDN噪声频率漂移特性,通过信号定位差分机制,联合最优极大似然估计,构建基于差分方式的方位角精度优化机制,降低定位误差。仿真结果表明,与采取单一节点接收方式的GDAM算法及采取单层环接收方式的NUPOS-1算法相比,在HSDN噪声干扰环境下,该算法具有更低的信号定位误差。展开更多
文摘For Future networks, many research projects have proposed different architectures around the globe;Software Defined Network(SDN) architectures, through separating Data and Control Layers, offer a crucial structure for it. With a worldwide view and centralized Control, the SDN network provides flexible and reliable network management that improves network throughput and increases link utilization. In addition, it supports an innovative flow scheduling system to help advance Traffic Engineering(TE). For Medium and large-scale networks migrating directly from a legacy network to an SDN Network seems more complicated & even impossible, as there are High potential challenges, including technical, financial, security, shortage of standards, and quality of service degradation challenges. These challenges cause the birth and pave the ground for Hybrid SDN networks, where SDN devices coexist with traditional network devices. This study explores a Hybrid SDN network’s Traffic Engineering and Quality of Services Issues. Quality of service is described by network characteristics such as latency, jitter, loss, bandwidth,and network link utilization, using industry standards and mechanisms in a Hybrid SDN Network. We have organized the related studies in a way that the Quality of Service may gain the most benefit from the concept of Hybrid SDN networks using different algorithms and mechanisms: Deep Reinforcement Learning(DRL), Heuristic algorithm, K path partition algorithm, Genetic algorithm, SOTE algorithm, ROAR method, and Routing Optimization with different optimization mechanisms that help to ensure high-quality performance in a Hybrid SDN Network.
文摘为解决当前5G无线网络中高速移动节点信号定位不准确和高稳定分布噪声(high stable distribution noise,HSDN)等特殊偏移噪声降低定位准确性的问题,提出基于电控旋转序列位移接收结构的5G无线网络信号定位算法。利用单一节点接收结构及环接收结构并采取分层方式,构建电控旋转序列位移接收结构;基于待定位信号与中央基站及各层接收节点之间的切线关系,初步估计待测信号方位;针对信号发射环境中的HSDN噪声频率漂移特性,通过信号定位差分机制,联合最优极大似然估计,构建基于差分方式的方位角精度优化机制,降低定位误差。仿真结果表明,与采取单一节点接收方式的GDAM算法及采取单层环接收方式的NUPOS-1算法相比,在HSDN噪声干扰环境下,该算法具有更低的信号定位误差。
