Mobility information for resource management in wireless ATM networks

User mobility poses a significant technical challenge to network resource management in wireless ATM (Asynchronous Transfer Mode) networks. In order to guarantee quality of service (QoS) to mobile users and to achieve a high efficiency in network resource management, the information of mobile users' handoff at a future moment is essential for statistical multiplexing. This paper develops a novel fuzzy logic inference system to estimate the user mobility information for a wireless ATM network which uses a direct sequence code division multiple access (DS/CDMA) protocol. The estimation is based on measured pilot signal strengths from a number of the nearest base stations by the mobile user. Numerical results are presented to demonstrate the performance of the proposed technique under various path losses and channel shadowing conditions. The proposed technique can achieve simplicity, accuracy and low cost.     

一种适用于WMSNs传输机制的信道盲估计方法

虚拟MIMO是一种克服无线多媒体传感器网络信道多径衰落和降低节点能耗的有效方法,但现有文献均假设信道状态信息未知或仅知道信道的统计特性,源节点则无法根据信道的实时信息动态调整传输策略。针对WMSNs信道特点,提出一种适用于WMSNs传输机制的信道盲估计方法以获得节点间信道信息进而确定传输策略,通过构造权值衰减联合决策函数,将节点间的信道盲估计问题转化为一个无约束优化问题,在此基础上设计了一种快速收敛的迭代算法以降低运算代价。该方法仅依赖小数据量就可正确估计出节点间信道,具有对抗信道时变特性的能力;理论推导与仿真表明该算法的收敛速度和运算量均能符合降低节点能耗的要求。     

基于辅助数据的OFDM时间同步算法

为了实现OFDM(Orthogonal Frequency Division Multiplexing,正交频分复用)系统在任意载波频偏下的高精度同步,本文提出了一种基于辅助数据的系统定时偏移估计方法.首先,在高斯白噪声信道下,基于不具有特殊结构的辅助数据,推导了最大似然准则下的最佳同步算法;然后针对最大似然法复杂度过高的问题,设计了计算复杂度降低的次优方法;最后,采用蒙特卡罗仿真,在频率选择性瑞利衰落信道下,对得到的新算法进行了评估.实验结果表明,新方法的定时性能显著优于传统算法.     

Probability density estimation from optimally condensed data samples

The requirement to reduce the computational cost of evaluating a point probability density estimate when employing a Parzen window estimator is a well-known problem. This paper presents the Reduced Set Density Estimator that provides a kernel-based density estimator which employs a small percentage of the available data sample and is optimal in the L/sub 2/ sense. While only requiring /spl Oscr/(N/sup 2/) optimization routines to estimate the required kernel weighting coefficients, the proposed method provides similar levels of performance accuracy and sparseness of representation as Support Vector Machine density estimation, which requires /spl Oscr/(N/sup 3/) optimization routines, and which has previously been shown to consistently outperform Gaussian Mixture Models. It is also demonstrated that the proposed density estimator consistently provides superior density estimates for similar levels of data reduction to that provided by the recently proposed Density-Based Multiscale Data Condensation algorithm and, in addition, has comparable computational scaling. The additional advantage of the proposed method is that no extra free parameters are introduced such as regularization, bin width, or condensation ratios, making this method a very simple and straightforward approach to providing a reduced set density estimator with comparable accuracy to that of the full sample Parzen density estimator.     

一种基于OFDM信号的多传感器测距算法研究

位置业务在未来移动通信系统中起到重要作用,引起了人们的广泛关注和研究。而正交频分复用(OFDM)技术由于其良好的抗多径衰落能力和较高的频带利用率,已被广泛应用于数字音频广播(DAB)、数字视频广播(DVB)、无线局域网等,成为下一代无线通信系统的核心技术之一。在经典的时间同步算法最大似然(ML)算法的基础上,结合一种整体相关的OFDM定时估计算法,提出了一种有效利用截短循环前缀(CP)相关来进行定时估计测距的改进算法。分别在高斯白噪声和多径衰落信道下进行仿真,结果表明:改进算法在任一信道下都有较好的性能。     

A new method for solving the problem of the mean estimation when the underlying regression function is discontinuous

In the context of finite population survey sampling, we propose a new model-based mean estimator, when the function that links the variables is discontinuous. The available estimators of the mean based on nonparametric regression are derived under the assumption that the regression function is continuous. We propose a new approach to adjust for the effect of discontinuity on regression estimation of the mean. The performance of the proposed estimator is analysed through a simulation study because the theoretic study of asymptotics is not possible. In the literature, the new estimator requires more computational cost than others, but the simulation experiments indicate that the proposed method has higher efficiency than other traditional parametric and nonparametric regression methods.     

Personness estimation for real-time human detection on mobile devices

One aim of detection proposal methods is to reduce the computational overhead of object detection. However, most of the existing methods have significant computational overhead for real-time detection on mobile devices. A fast and accurate proposal method of human detection called personness estimation is proposed, which facilitates real-time human detection on mobile devices and can be effectively integrated into part-based detection, achieving high detection performance at a low computational cost. Our work is based on two observations: (i) normed gradients, which are designed for generic objectness estimation, effectively generate high-quality detection proposals for the person category; (ii) fusing the normed gradients with color attributes improves the performance of proposal generation for human detection. Thus, the candidate windows generated by the personness estimation will very likely contain human subjects. The human detection is then guided by the candidate windows, offering high detection performance even when the detection task terminates prior to completion. This interruptible detection scheme, called anytime detection, enables real-time human detection on mobile devices. Furthermore, we introduce a new evaluation methodology called time-recall curves to practically evaluate our approach. The applicability of our proposed method is demonstrated in extensive experiments on a publicly available dataset and a real mobile device, facilitating acquisition and enhancement of portrait photographs (e.g. selfie) on widespread mobile platforms.     

无线传感网中移动节点的自适应定位算法

介绍了一种在无线传感网中针对移动节点的自适应定位算法,通过建立移动节点运动模型,借助运动模型估计信息辅助移动节点的定位,解决了只有2个或1个信标节点的无法定位的问题,本算法能够适应不同移动节点的运动状态,动态地调整定位时间间隔,从而既能提高节点定位的精度,也能在保证定位误差的情况下降低定位频率,节省定位能量的消耗,延长整个无线传感网的生存时间。     

基于FPGA的OFDM时频同步系统的设计与仿真

同步技术是OFDM中的一项关键技术.但由于无线信道的随机性,传统方法在门限设定上无法兼顾判断准确性和计算复杂度两方面性能.针对这一问题,提出了一种将相对门限和浮动门限相结合的同步判决方法,它能够在保持判断准确性的同时降低计算时的复杂度.此外,通过交叠计算训练序列相关值的方法来提高频偏估计精度.在此基础上,详细阐述了算法关键模块的电路结构和硬件实现.仿真结果表明该算法在多径信道环境下具有较高的时间同步和频偏估计精度,能够有效地降低硬件资源消耗,节省传输带宽.     

改进CAZAC序列的OFDM时频同步算法

针对先前一些算法存在定时模糊以及存在较大干扰峰干扰的问题,提出了一种基于加权CAZAC序列的OFDM系统同步方案。利用训练序列共轭对称特性进行符号定时和频偏估计。利用训练序列延迟相关和对称相关的特点进行定时和粗频偏估计,能够有效抑制干扰从而得到更好的估计性能。利用循环前缀的冗余信息进行小数倍频偏精细估计。此外分析了整数倍频偏对时域CAZAC序列的影响。采用时域循环移位进行整数倍频偏估计。通过与其他算法的仿真对比可以得出,多径衰落信道下所提算法能够取得较高的定时准确率和频偏估计精度。     

An adaptive least squares channel estimation based on conjugate gradient approach for multipath fading CDMA systems

Multiple access interference (MAI) is the main factor affecting the performance of channel estimation techniques for code division multiple access (CDMA) systems. Although, several multi-user channel estimation algorithms have been proposed to mitigate MAI, these algorithms require high computational complexities. In this paper, we address the problem of iterative least squares (LS) mobile channel estimation at high channel efficiency that requires a short training sequence along with the spreading sequences. We employ an efficient iterative method based on conjugate gradient (CG) algorithm to reduce the computational complexity of the estimation method. Computer simulations illustrate that the proposed method performs almost identical to the exact LS estimate for reasonable training lengths.     

A 3-degree of freedom binary search pose estimation technique

This paper describes a novel, binary search pose estimation (BSPE) technique for poses constrained to 3 degrees of freedom (DOF). The technique requires three fiduciary marker points and operates by minimising the angular DOF through a binary search driven algorithm. The technique is less computationally intensive than the standard closed form solutions and quickly converges to a solution which can be verified if additional fiduciary points are available. Evaluation for the technique is presented through a comparison to pose from orthography and scaling with iterations (POSIT) using synthetic data and a real-world application, where the technique is used for pose estimation of a mobile robot with respect to a motor vehicle for inspection purposes.     

一种有效的LTE下行残留频偏估计方法*

针对LTE系统对载波频偏敏感的问题,提出了一种适用于LTE下行的残留频偏估计方法。该方法利用时域相邻的主同步信道(P-SCH)和辅同步信道(S-SCH)的频域信道估计值的共轭相关运算来估计载波频率的残留偏差。理论分析表明,该算法估计范围小,但估计精度高,且运算量少,实现复杂度低。同时仿真验证了该算法在高斯加性信道(AWGN)和多径衰落信道下均有良好的性能。     

一种简化的OFDM信道估计方法

OFDM（JE交频分复用）技术是无线通信领域的重要技术,研究信道估计精度对系统性能有着直接的影响。提出了基于导频插入的LS（最小二乘法估计）、MMSE（最小均方误差估计）和SVD（奇异值分解）信道估计方法。并针对MMSE算法有很好的性能但复杂度大,LS算法复杂度低但性能受到限制,提出了一种改进方法。采用广义平稳非相关散射下的多径时变瑞利信道模型,对各个算法进行了仿真试验,得出不同算法的信道估计中误比特曲线,比较了各个算法的性能。仿真结果表明：改进的算法有好的性能和低的复杂度。     

引入加权系数消除ICI的二次快时变信道估计算法

针对高速移动环境下由多普勒频移引起的子载波间干扰（inter-carrier interference,ICI）,提出一种引入加权系数消除ICI的二次快时变信道估计算法。该算法进行两次信道估计,第一次估计用于提供信道状态信息,在保证符号率的前提下消除ICI,第二次估计在ICI消除的情况下进行,可以提高信道估计精度;并对简化的并行干扰消除（parallel interference cancellation,PIC）算法进行改进,基于最小均方误差准则引入加权系数,使得ICI消除后的残余误差最小。仿真结果和理论分析达到很好的一致性,当信道信噪比为0 dB时,归一化均方误差（normalized mean square error,NMSE）性能增益最大约为0.0714,提高了快时变信道估计的精度。     

异构无线网络中基于自更新哈希链的不可否认性计费协议

异构无线网络融合是下一代网络发展的必然趋势,UMTS、LTE、WiMAX和WiFi等无线网络既相互竞争,又相互补充.安全计费是异构无线网络商用面临的主要挑战.提出了一种在异构网络中基于自更新哈希链的不可否认性计费协议,即通过采用新颖的自更新哈希链,移动终端能够快速更新可用哈希链,保证连续的快速认证.提出的方案能够提供解决计费纠纷的证据,实现计费的不可否认性、机密性和准确性.理论分析和性能仿真表明,提出的方案具有计算开销低、通信时延小等优势,满足异构无线网络的性能要求.     

Recommender system for mobile users

As predicted, trillions of devices and billions of services will be integrated into Internet of Things (IoT), where most value added applications rely on wireless physical links. In this paper, we develop a recommender system to overcome the challenges of large-scale mobile IoT. The proposed recommender system socially matches wireless devices to communicate and share their contents based on similarities, distance, velocity, wireless channel quality and remaining energy. The physical layer connections are realized by device-to-device spectrum sharing techniques, and we accordingly designed a cooperative multicast service case to make full use of the wireless broadcasting nature. A “green communication” orientated algorithm is proposed to allocate power resources, adaptively adjust data rate and recommend partners as mobile relays. Simulation results show that the proposed system can efficiently utilize the wireless resource of mobile IoT and appropriately recommend partners to assist more users into IoT services.     

基于信噪比估计和矢量平均的干涉仪抗噪声测向方法

针对低信噪比条件下干涉仪测向准确度低的问题,提出了一种基于信噪比估计和相位差矢量平均的自适应测向方法。本文方法通过对多次测量的相位差复数矢量求平均来提高相位差的测量精度和稳定性,从而提高测向性能。提出的自适应准则通过估计来波信噪比,可快速确定不同信噪比下矢量平均所需样本量,使处理后信号达到设定信噪比阈值,获得稳定的测向准确度。分析了信噪比阈值对本方法测向性能的影响。本文方法计算复杂性小,对测向实时性影 响小。理论分析和仿真结果表明：本方法在低信噪比条件下可以达到很高的测向准确度,对低信噪比条件下的测向性能改善明显。     

OFDM系统中基于因子图的信道估计算法

为提高正交频分复用(OFDM)系统的信道估计精度,根据频谱资源的无陑信道特性,提出基于因子图的 OFDM 系统信道估计算法,包括二维联合信道估计算法和2个级联的一维信道估计算法。将时变频率选择性衰落信道建模为一阶自回归模型,使信道参数之间的交互信息近似为高斯分布,利用和积算法实现OFDM系统的联合信道估计和符号检测。仿真结果表明,该信道估计算法能够以较低的计算复杂度逼近最优的估计性能。     

基于多维标度的动态网络选择算法

异构无线网络的网络选择问题中,除了考虑多属性的网络参数外,还需考虑无线信道的时变性特征。针对这两个考虑因素,提出了一种基于多维标度的新的动态网络选择算法,可将多属性判决的网络选择问题近似转化为一个低维的拟合构图,并能反映不同时刻同个网络的变化。仿真结果表明,在不考虑负载的情况下,提出的算法能有效地选择出适合于移动终端当前业务的网络,并能在低维坐标系中直观地观察网络间的区别。