Superimposed training for channel estimation of OFDM modulated amplify-and-forward relay networks

This paper is concerned with the problem of channel estimation for amplify-and-forward （AF） cooperative relay networks with orthogonal frequency division multiplexing （OFDM） modulation. The algorithm is based on both the least square （LS） and minimum mean square error （MMSE） technique with a superimposed training strategy. Specifically, both the source and relay superimpose their own training signal onto data stream prior to transmission so as to estimate the separate channel state information of the source to relay link and the relay to destination link. We also present the performance analysis and derive the approximated closed-form expressions for the MSE of separate channel estimation of source to relay link and the relay to destination link, respectively, from which we compute the optimal training signal as well as the relay power-amplification factor. To further improve the performance of channel estimation, we adopt a weighted average process to enhance the estimation performance over multiple OFDM blocks, from which we compute the optimal tracking factor. Simulation results are provided to corroborate the proposed scheme.     

Joint relay selection and power allocation for amplify-and-forward two-path relaying networks

This paper presents a joint relay selection and power allocation scheme for amplify-and-forward two-path relaying networks,in which diferent relay nodes forward information symbols alternatively in adjacent time slots.Our approach is based on the maximization of the received signal-to-noise ratio under total power consumption by the transmission of the symbol.We show that in spite of inter-relay interferences,the maximization problem has a closed-form solution.Simulation results explicitly indicate that the performance of proposed approach outmatches the existing methods including equal power allocation and one-path relaying.     

Low-complexity distributed differential spacetime coding scheme for amplify-and-forward cooperative networks

By introducing a space-time coding scheme based on amicable orthogonality, we develop a distributed differential space-time coding scheme with the amplify-and-forward (AF) method for wireless cooperative networks. The scheme requires no knowledge of the channel state information at both transmitters and receivers, and effectively decreases the realization complexity due to no channel estimation. Moreover, it has lower decoding complexity and higher coding advantage than the existing scheme, thus avoiding the shortcoming of exponential decoding complexity of some existing schemes. According tothe pairwise error probability (PEP) analysis of the system, the power allocations of source and relay terminals are jointly optimized, and as a result, the PEP is minimized, which will provide a helpful guideline for system design. Numerical calculation and simulation results show that the developed scheme is superior to the existing scheme. Moreover, the scheme with optimal power allocation yields obvious performance improvement over that with equal power allocation. Supported partially by the China Postdoctoral Science Foundation (Grant No. 2005038242) and the startup fund of Nanjing University of Aeronautics and Astronautics (Grant No. S0855-041)     

Optimal power allocation for cognitive relay networks: amplify-and-forward versus selection relay

We consider a cognitive relay network which is defined by a source,a destination,and cognitive relay nodes and primary user nodes.In this network,a source is assisted by cognitive relay nodes which allow coexisting with primary user nodes by imposing severe constraints on the transmission power so that they operate below the noise floor of primary user nodes.In this paper,we mainly study the power allocation strategies of this system to minimize the outage probability subject to total and individual power c...     

Wavelet networks for sensor signal classification in flank wear assessment

It is known that the force and vibration sensor signals in a turning process are sensitive to the gradually increasing flank wear. Based on this fact, this paper investigates a flank wear assessment technique in turning through force and vibration signals. Mainly to reduce the computational burden associated with the existing sensor-based methods for flank wear assessment, a so-called wavelet network is investigated. The basic idea in this new method is to optimize simultaneously the wavelet parameters (that represent signal features) and the signal-interpretation parameters (that are equivalent to neural network weights) to eliminate the feature extraction phase without increasing the computational complexity of the neural network. A neural network architecture similar to a standard one-hidden-layer feedforward neural network is used to relate sensor signal measurements to flank wear classes. A novel training algorithm for such a network is developed. The performance of this n ew method is compared with a previously developed flank wear assessment method which uses a separate feature extraction step. The proposed wavelet network can also be useful for developing signal interpretation schemes for manufacturing process monitoring, critical component monitoring, and product quality monitoring.     

细胞信号转导网络输入信号的最优实验设计

细胞信号转导网络的结构复杂,规模庞大,具有高度非线性,数学模型的参数和变量数目很多,有效估计未知参数是系统生物学主要的研究问题之一。提出以Fisher信息矩阵和估计参数的协方差矩阵为基础,利用不同的优化实验设计方法,研究NF-κB信号转导网络输人信号IKK初浓度的最优设计问题,分析模型参数的置信区间,为参数估计设计优化实验。仿真结果表明,采用E-最优实验设计准则,获得输入信号IKK初浓度为0.06μM,能够提供较多的实验信息,提高参数估计的精度。     

Collaborative signal processing for target tracking in distributed wireless sensor networks

Target tracking, especially visual target tracking, in complex situations is challenging, which is always performed in single-view system. Because of the conflict between resolution and tracking range, however, single-view tracking is not robust and accurate. This paper presents a distributed multi-view tracking system using collaborative signal processing (CSP) in distributed wireless sensor networks (DWSNs). In the proposed tracking system, target detection and classification algorithms are based on single-node processing and target tracking is performed in sink node, whereas target localization algorithm is carried out by CSP between multisensor. For conquering the disadvantages of client/server based centralized data fusion, a progressive distributed data fusion are proposed. Finally, an indoor target tracking experiment is illustrated, and then tracking performance, execution time and energy consumption of progressive distributed data fusion are compared with client/server based centralized data fusion. Experimental results demonstrate that the CSP based distributed multi-view tracking system in DWSNs can accomplish multi-target extraction, classification, localization, tracking and association quickly and accurately with little congestion, energy consumption and execution time.     

水下传感器网络节点定位信号检测的设计

针对水下环境的特殊性和水声定位系统的要求,为降低检测水声定位信号的复杂度和硬件资源消耗,能够准确检测定位基站发射的水声定位信号,为目标基站的定位提供实时、快速的时延信息,根据水声定位信号的特点,采用码元匹配检波和编码定位信号相关的方法,确定水声定位信号的到达时间,并以FPGA为平台利用VHDL语言对水声定位信号的检测设...     

基于Morris法的细胞信号转导网络全局灵敏性分析

细胞信号转导网络的结构复杂和动力学参数的不确定影响着系统的动态特性,如何定量系统输出与参数变化的关系,以及参数间的相互影响,已经成为系统生物学研究的重要问题之一。以TNF-α诱导NF-kB信号转导网络作为研究对象,采用Morris法的全局灵敏性分析技术,研究参数变化对输出NF-kBn的影响,确定参数的全局灵敏度。仿真结果表明,系统输出NF-kBn对于参数θ_(28)、θ_(36)、θ_(52)、θ_(61)、θ9、θ_(29)、θ_(19)和θ_(62)比较敏感,故它们影响着NF-kBn的振荡特性,从而说明这些参数是NF-kB信号转导网络关键的速率常数。     

认知无线电中基于比例公平的资源分配方案

在基于OFDM的认知无线电网络中,认知用户采用放大转发（Amplify-and-Forward,AF）协作模式进行数据传输。提出了功率和子载波分配及配对的优化算法,认知用户在子载波上的总发射功率是有限的,同时对授权用户造成的干扰必须低于门限值。采用拉格朗日对偶分解法和次梯度法对功率分配算法进行求解,并对子载波分配及配对算法进行了推导。仿真结果表明,与最大化总速率（maximize-total）公平性和最大化最小速率（maximize-worst）公平性相比,比例公平性（maximize-pro-fair）是一个能够使效率与公平性更加均衡的标准;在低信噪比时,就提高系统传输效率以及资源分配公平度指标而言,该算法依然优于其他三种分配方案。     

2D sparse signal recovery via 2D orthogonal matching pursuit

Recovery algorithms play a key role in compressive sampling (CS).Most of current CS recovery algo-rithms are originally designed for one-dimensional (1D) signal,while many practical signals are two-dimensional (2D).By utilizing 2D separable sampling,2D signal recovery problem can be converted into 1D signal recovery problem so that ordinary 1D recovery algorithms,e.g.orthogonal matching pursuit (OMP),can be applied directly.However,even with 2D separable sampling,the memory usage and complexity at the decoder are still high.This paper develops a novel recovery algorithm called 2D-OMP,which is an extension of 1D-OMP.In the 2D-OMP,each atom in the dictionary is a matrix.At each iteration,the decoder projects the sample matrix onto 2D atoms to select the best matched atom,and then renews the weights for all the already selected atoms via the least squares.We show that 2D-OMP is in fact equivalent to 1D-OMP,but it reduces recovery complexity and memory usage significantly.What’s more important,by utilizing the same methodology used in this paper,one can even obtain higher dimensional OMP (say 3D-OMP,etc.) with ease.     

An inexact continuation accelerated proximal gradient algorithm for low n-rank tensor recovery

The low n-rank tensor recovery problem is an interesting extension of the compressed sensing. This problem consists of finding a tensor of minimum n-rank subject to linear equality constraints and has been proposed in many areas such as data mining, machine learning and computer vision. In this paper, operator splitting technique and convex relaxation technique are adapted to transform the low n-rank tensor recovery problem into a convex, unconstrained optimization problem, in which the objective function is the sum of a convex smooth function with Lipschitz continuous gradient and a convex function on a set of matrices. Furthermore, in order to solve the unconstrained nonsmooth convex optimization problem, an accelerated proximal gradient algorithm is proposed. Then, some computational techniques are used to improve the algorithm. At the end of this paper, some preliminary numerical results demonstrate the potential value and application of the tensor as well as the efficiency of the proposed algorithm.     

智能传感器侦察网络中的目标声信号识别算法

针对智能传感器侦察网络中的地面目标识别问题,提出了一种基于智能计算方法的地面目标声信号识别算法。基于智能计算方法设计识别系统,直接利用信号特征的变化范围作为分类特征,并结合能够处理定性输入的粗神经网络分类算法,有效地克服目标信号的不确定性问题,提高识别系统的识别率和稳定性。     

Cyclic maximization of non-Gaussianity for blind signal extraction of complex-valued sources

This article presents a new algorithm for the blind extraction of communications sources (complex-valued sources) through the maximization of negentropy approximations based on nonlinearities. A criterion based on the square modulus of a nonlinearity of the output is used. We decouple the arguments of the criterion so that the algorithm maximizes it cyclically with respect to each argument by means of the Cauchy-Schwarz inequality. A proof of the ascent of the objective function after each iteration is also provided. Numerical simulations corroborate the good performance of the proposed algorithm in comparison with the existing methods.     

基于无线传感器网络的光电信号采集系统设计

针对目前一些大型构件吊装系统中光电信号采集系统存在的布线复杂,可扩展性差,信号采集和转换效率低,数据传输能力弱等问题.分析了传统光电采集系统的缺陷,利用无线传感器网络技术,提出了一种新的光电信号采集系统,该系统以STM32F103ZET6为主控器,nRF24L01为无线传输模块.给出了系统的软硬件实现方法,并将其应用于硅光电池(PC50-6)作为传感器部件的信号采集.实验结果表明:该新型系统在信号采集的速度和精度方面有了明显的提高,能够满足系统的要求,为无线传感网络在光电信号采集应用方面做了一定的探索.     

一种无线传感器网络的盲源分离算法

无线网络协议的校验纠错机制繁重复杂,加重了通信带宽的负担.盲源分离无需任何先验知识就可重构出源信号,成为无线传感器领域的研究热点.但网络部署中,传感器信号的量化位数和Modem个数难以确定.针对上述问题,提出一种基于平方根容积卡尔曼滤波(SRCKF)的盲源分离算法.算法根据空间状态方程和测量方程,用平方根容积卡尔曼估计分离向量.实验结果表明:SRCKF算法在保持极高分离精度的情况下,能有效计算出量化位数和Modem个数的最优值,SRCKF算法效率是同类无先导卡尔曼滤波(UKF)算法效率的1.3578倍.     

移动无线传感器网络连通性自主恢复算法

无线传感器网络(WSNs)中关键节点故障会将网络分割成多个不连通的分区,给网络性能带来严重影响.而无线传感器网络往往布置在人工难以干预的偏僻恶劣环境中,网络连通性的自主恢复尤其重要.提出一种自主网络分区检测和连通性恢复(APDCR)策略,容忍无线传感器网络中关键节点的故障.APDCR首先基于1跳邻居和部分2跳邻居信息提出一种新的关键节点识别算法,然后给出关键节点的备用节点的选取算法和网络连通性恢复算法,最后扩展算法处理两个节点同时故障时网络不连通问题(2-APDCR).模拟实验结果表明了算法APDCR的有效性.     

盲信号处理中FastICA算法的IP核设计

针对盲信号处理中FastICA算法处理速度慢、性能差的问题,提出使用FPGA实现FastI-CA算法的方案,以提高FastICA算法的处理能力。设计了基于Avalon总线的FastICA IP核,嵌入到SoPC和ASIC设计中。仿真测试结果表明,FastICA IP核实现了盲信号分离,处理速度是PC的20倍,满足了高速盲信号处理的需要。