Efficient multicast search under delay and bandwidth constraints

The issue of a multicast search for a group of users is discussed in this study. Given the condition that the search is over only after all the users in the group are found, this problem is called the Conference Call Search (CCS) problem. The goal is to design efficient CCS strategies under delay and bandwidth constraints. While the problem of tracking a single user has been addressed by many studies, to the best of our knowledge, this study is one of the first attempts to reduce the search cost for multiple users. Moreover, as oppose to the single user tracking, for which one can always reduce the expected search delay by increasing the expected search cost, for a multicast search the dependency between the delay and the search cost is more complicated, as demonstrated in this study. We identify the key factors affecting the search efficiency, and the dependency between them and the search delay. Our analysis shows that under tight bandwidth constraints, the CCS problem is NP-hard. We therefore propose a search method that is not optimal, but has a low computational complexity. In addition, the proposed strategy yields a low search delay as well as a low search cost. The performance of the proposed search strategy is superior to the implementation of an optimal single user search on a group of users. Amotz Bar-Noy received the B.Sc. degree in 1981 in Mathematics and Computer Science and the Ph.D. degree in 1987 in Computer Science, both from the Hebrew University, Israel. From October 1987 to September 1989 he was a post-doc fellow in Stanford University, California. From October 1989 to August 1996 he was a Research Staff Member with IBM T. J. Watson Research Center, New York. From February 1995 to September 2001 he was an associate Professor with the Electrical Engineering-Systems department of Tel Aviv University, Israel. From September 1999 to December 2001 he was with AT research labs in New Jersey. Since February 2002 he is a Professor with the Computer and Information Science Department of Brooklyn College - CUNY, Brooklyn New York. Zohar Naor received the Ph.D. degree in Computer Science from Tel Aviv University, Tel Aviv, Israel, in 2000. Since 2003 he is with the University of Haifa, Israel. His areas of interests include wireless networks, resource management of computer networks, mobility, search strategies, and multiple access protocols.     

多频段高动态信号载波频偏捕获方法研究

研究了多频段高动态信号载波频偏问题,分析了多频段给频偏捕获带来的困难,针对低数据率、大载波频偏的情况,讨论了抽取倍数对捕获范围和精度的影响,并提出对于不同频段,通过改变相应的抽取倍数来满足不同的频偏和精度,再进行最大功率搜索法估计频偏的捕获方案。仿真结果表明,与传统方法进行比较,该方案在低信噪比下条件下估计性能更好,能够适应UHF和S频段,在数据率9.6 kb/s的条件下,估计精度在UHF频段下为300Hz,在S频段下为600Hz。     

基于高速ADC和FPGA的IR-UWB相干接收机设计

为了探索超宽带(UWB)技术的空间应用,设计了一款脉冲无线超宽带(IR-UWB)的全数字化相干接收机。本系统利用高速采样芯片ADC08D1000对IR-UWB信号进行双通道交织采样,接下来利用Xilinx FPGA对采样数据进行降速处理。按照二分搜索、相干捕获、脉冲跟踪的方案进行接收系统设计,并对该接收机进行了实际测试。最终实现了1 ns脉宽的IR-UWB信号的正确接收和1 Mb/s码速率的OOK编码通信。     

相控阵雷达搜索参数研究

通过对相控阵雷达搜索参数研究,从概念和系统的角度,提出了监视空域与预警时间、搜索时间与搜索帧周期、跟踪数据率、波位编排方式、确认时间、跟踪容量等参数的选择原则和确定方法;分析了天线的扫描角增益损失,给出了相应的能量补偿办法;建立了确认概率、起始跟踪距离的概念,按照信噪比与确认概率、积累检测概率的关系将雷达对目标的发现过程划分为4个阶段。     

预警信息引导条件下雷达最优搜索空域研究

提出了一种基于预警引导信息的弹道导弹防御跟踪制导雷达搜索空域最优划分模型,从搜索空域的静态划分和动态更新2个方面同时进行最优化,实现了弹道目标高速运动条件下,跟踪制导雷达对目标分布区域的连续有效覆盖,提高了雷达对目标的截获性能。最后给出了仿真结果,验证了该方法的有效性。     

移动通信系统中功率控制研究

在阐述功率控制在移动通信系统中的发展过程的基础上，着重研究了WCDMA系统的功率控制，最后对功率控制未来的研究方向做了简要的分析。     

基于BAS-BP-Bagging模型的光纤陀螺温度补偿

为提高光纤陀螺的输出精度,以天牛须搜索算法(BAS)优化后的BP神经网络模型为基学习器,采用Bagging并行集成学习算法建立了BAS-BP-Bagging温度补偿模型,并对某型号光纤陀螺进行了温度补偿实验。实验结果表明,在-40～+60℃温度变化环境下,该方法补偿后的光纤陀螺温度漂移相较于补偿前减小了近80%,相较于多项式补偿算法减小了55%,相较于BP神经网络补偿算法减小了30%左右。同时该模型在对新鲜样本的补偿过程中表现出了较为优越的泛化性能。     

基于时间可预测性的差分搜索盲信号分离算法

针对基于仿生智能优化的盲信号分离算法计算量偏大的问题,提出了一种新的基于差分搜索的盲信号分离算法。采用信号在时间上的可预测性度量作为目标函数,使用差分搜索算法对目标函数进行优化求解。利用去相关消源方法从混合信号中去除每次分离出的源信号成分,通过逐次分离最终实现对所有源信号的成功恢复。仿真实验表明,所提算法可以有效实现对混合信号的盲分离。与其他算法相比,该算法在保证了更高分离精度的同时,具有更低的运算量。     

遗传算法在成像卫星计划编制中的应用

采用遗传算法（Genetic Algorithms）实现成像卫星计划编制中的星地资源优化分配。成像卫星计划编制是与资源相关的规划调度问题,结合使用约束条件进行星上资源和地面站接收资源的分配与调度,制定合理可行的卫星摄影计划和地面站跟踪接收计划。在介绍遗传算法的基本原理和方法的基础上,详细阐述了遗传算法在成像卫星计划编制中的具体应用实践。基于设计实现的成像卫星计划编制原型系统,采用合适的适应度函数、交叉算子和变异算子,通过仿真实验获得了良好的优化效果。