前馈PMD补偿实验中从偏振度椭球获得差分群时延的研究

运用偏振度(DOP)椭球法从实验的有限样点数据中获得了差分群时延和偏振主态方向,这为前馈补偿实验提供了最关键的技术因素.在实验中获得了偏振主态方向和DGD,而且当DGD较小时,从实验所获得的DGD值与理论模拟的变化趋势基本一致.结果表明当DGD较小时,可以用偏振度椭球法从实验上得到正确的DGD和偏振主态方向.     

10 Gb/s光纤通信系统一阶偏振模色散动态补偿系统反馈控制的实现

在偏振模色散(PMD)自动补偿技术中,如何根据反馈信号得到相应的控制信号,使补偿速度跟随偏振模色散变化始终是该技术的一个核心问题。提出了一种新颖的自适应抖动跟踪算法,完成了以微波信号为反馈的多自由度的一阶偏振模色散自动反馈补偿系统的跟踪补偿实验。算法成功地解决了传统算法在跟踪搜索过程中易陷入局部极值的问题,有效地克服了系统中的重要控制器件偏振控制器的磁滞现象以及动态补偿时跟踪搜索过程中易出现的瞬间恶化现象。实验结果表明该算法在对出现突发偏振模色散扰动后自动进行补偿的响应速度在ms量级,最快能达到1~2 ms。     

深空光通信中的图像信标捕获技术

为实现深空光通信链路建立过程中精确的对准,提出了一种深空光通信系统扩展信标的捕获方案。该方案以可视地球图像作为信标,在航天器上存储一幅信标图像作为参考图像,采用天线扫描的方式在各点对所瞄准的区域成像,利用像素扫描的方式,使参考图像和实际探测图像进行相关,在天线扫描完成以后,找出相关性最大的位置,即可认为在该点捕获到地球图像。在计算两图像相关系数的过程中,由于傅里叶梅林变换幅度谱具有伸缩及旋转不变性,因此利用傅里叶梅林变换即可消除两图像相关系数因为旋转和伸缩的影响。利用蒙特卡罗方法随机产生1000个视场,仿真结果表明,3σ内正确捕获到信标图像的概率为99.6%,表明这是一种可行的方法。     

塑料光纤纤端光场测试结果及分析

对纤端光场进行了理论分析,分别利用塑料光纤和多模光纤作为接收光纤对塑料光纤的出射纤端光场进行了测量,并与理论分析结果进行了比较,给出了合理的调和参数.传输距离较长的塑料光纤由于高阶模的泄漏,纤端光场分布非常接近高斯分布.实验结果表明了理论分析结果和调和参数选择的合理性.     

周期性管柱信道的声波通信技术研究综述

井下声波通信技术利用周期性管柱信道实时传输声波数据,相比于其他通信方式,具有适应性好、复杂度低、传输效率高等优点,在随钻地质导向、油水井监测等方面有广阔的应用前景,是发展智能油田的关键技术之一。本文主要综述上世纪90年代以来关于信道物理模型、信道容量、声信号调制与接收方法等关键问题的研究历程,并对通信系统的设计要点与国内外相关产品的现场应用进行梳理,为后续的技术改进提供依据。     

语音通信降噪研究

语音通信系统中,语音通过信道传输将不可避免地引入码间串扰和信号畸变,同时受到噪声污染。本文在分析自适应盲均衡算法CMA(constant modulus algorithm）和改进盲均衡算法的基础上,考虑到自适应盲均衡技术在语音噪声控制方面能力有限,将自适应盲均衡技术与小波包掩蔽阈值降噪算法联合使用,形成一种基带语音增强新方法。仿真试验结果显示自适应盲均衡技术可以使星座图变得清晰而紧凑,有效减小误码率。研究证实该方法在语音信号ISI和畸变严重情况下,在白噪及有色噪声不同的噪声环境中都具有稳定的降噪能力,消噪同时可获得汉语普通话良好的听觉效果。     

基于EKF的水下LD通信精对准控制算法

高速激光通信中接收机与光斑中心很难处于精对准状态,导致水下光通信链路难以稳定建立.首先采用蒙特卡洛仿真统计法分析激光光子在海水中传输的接收光强分布规律,再通过实验对接收端的光斑图像进行采样分析,利用曲线拟合得到接收器位置与接收光强的关系.仿真与实验结果表明:光束经过25 m的水下传输,接收光强分布仍近似为高斯分布.采用非线性估计算法(扩展卡尔曼滤波)与基本状态控制反馈理论,根据接收光强度估计接收器当前位置与最大光强处的距离,通过反馈算法实现接收端与光斑中心的主动跟踪对准.算法仿真结果显示,接收端对准误差在2 mm以下,稳定后接收效率超过98%.     

Advantage of Quantum Theory over Nonclassical Models of Communication

Quantum correlations provide dramatic advantage over the corresponding classical resources in several communication tasks. However, a broad class of probabilistic theories exists that attributes greater success than quantum theory in many of these tasks by allowing supra-quantum correlations in “space-like” and/or “time-like” paradigms. In this letter, a communication task involving three spatially separated parties is proposed where one party (verifier) aims to verify whether the bit strings possessed by the other two parties (terminals) are equal or not. This task is called authentication with limited communication, the restrictions on communication being: i) the terminals cannot communicate with each other, but (ii) each of them can communicate with the verifier through single use of channels with limited capacity. Manifestly, classical resources are not sufficient for perfect success of this task. Moreover, it is also not possible to perform this task with certainty in several nonclassical theories although they might possess stronger “space-like” and/or “time-like” correlations. Surprisingly, quantum resources can achieve the perfect winning strategy. The proposed task thus stands apart from all previously known communication tasks as it exhibits quantum advantage over other nonclassical strategies.     

The Optimal Power Allocation for Sum Rate and Energy Efficiency of Full-Duplex Two-Way Communication Network

Full-duplex (FD) transmission holds a great potential of improving the sum data rate of wireless communication systems. However, the self-interference introduced by the full-duplex transmitter brings a big challenge to enhance the energy efficiency. This paper investigates the power allocation problem in a full-duplex two-way (FDTW) communication network over an OFDM channel, aiming at improving the sum data rate and energy efficiency. We first characterize the sum rate and energy efficiency achieved in a single-carrier FDTW system. The optimal transmit power that achieves the maximal sum data rate is presented. The energy efficiency maximization problem is solved by using fractional programming. Then we further formulate sum rate and energy efficiency maximization problem in a multi-subcarrier FDTW system. In particular, the sub-optimal transmit power allocation which achieves a decent sum rate improvement is found by using a proposed iterative algorithm. By combining the iterative algorithm and fractional programming, we further maximize the energy efficiency of the multi-subcarrier system. With our proposed algorithm, we can easily obtain an optimal transmit power that approximates the global optimal solution. Simulation results show that using the obtained optimal transmit power allocation algorithm can significantly improve the sum rate and energy efficiency in both single-carrier and multi-subcarrier systems.     

Multi-Model Communication and Data Assimilation for Mitigating Model Error and Improving Forecasts

Models for weather and climate prediction are complex, and each model typi-cally has at least a small number of phenomena that are poorly represented, such as perhaps the Madden-Julian Oscillation (MJO for short) or El Ni\~{n}o-Southern Oscillation (ENSO for short) or sea ice. Furthermore, it is often a very challenging task to modify and improve a complex model without creating new deficiencies. On the other hand, it is sometimes possible to design a low-dimensional model for a particular phenomenon, such as the MJO or ENSO, with significant skill, although the model may not represent the dynamics of the full weather-climate system. Here a strategy is proposed to mitigate these model errors by taking advantage of each model''s strengths. The strategy involves inter-model data assimilation, during a forecast simulation, whereby models can exchange information in order to obtain more faithful representations of the full weather-climate system. As an initial investigation, the method is examined here using a simplified scenario of linear models, involving a system of stochastic partial differential equations (SPDEs for short) as an imperfect tropical climate model and stochastic differential equations (SDEs for short) as a low-dimensional model for the MJO. It is shown that the MJO prediction skill of the imperfect climate model can be enhanced to equal the predictive skill of the low-dimensional model. Such an approach could provide a route to improving global model forecasts in a minimally invasive way, with modifications to the prediction system but without modifying the complex global physical model itself.