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)     

门限决策的AF中继选择协作网络性能分析

中继选择能有效地克服信道衰弱影响,提高分集效果,但全天候协作降低了系统的带宽效率。研究了Rayleigh信道条件下一种新的基于门限决策的多中继选择系统并分析了系统性能,推导了M-PSK信号多中继选择系统的SER闭式表达式和系统带宽效率表达式。实验结果显示,在低信噪比和较少候选中继场景,提高协作门限,可提高系统带宽利用率;高信噪比和较多候选中继场景,适度降低协作门限,可有效提高系统平均SER性能。     

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...     

在认知中继网络中基于放大转发与最佳中继的最优功率分配算法

由于受认知无线电与中继通信技术的启发,提出了一种认知中继网络模型.该模型由源节点、目的节点、认知中继节点及主用户(primary user,PU)构成.认知中继节点以与PU共存的方式为源节点辅助传输信息到目的节点,只要保证其对PU通信造成的干扰在PU干扰门限值以下.假设源节点、目的节点和认知中继节点之间的瞬时信道边信息(channel side information,CSI)和认知中继节点到主用户之间的均值信道增益已知的前提下,研究该模型中的认知中继节点分别采用放大转发(amplify-and-forward,AF)和基于AF的中继选择(selection AF,S-AF)下的功率分配策略,该策略以最小化系统中断概率为目标,同时也满足认知中继节点的发射功率约束(包括总发射功率和个体发射功率约束)和对主用户的干扰功率约束.最后,通过数值仿真来验证推导出的功率分配策略.仿真结果表明:本文提出的最优功率分配策略,无论在AF,还是S-AF下,均能明显的改善系统的中断性能和平均吞吐量;同时在S-AF下最优分配策略可以得到更高的平均吞吐量,因此中断概率更小.     

多向中继网络中的协作复用技术

为提升多向中继网络的频谱效率,提出一种基于协作复用的传输技术,多个配置单天线的中继节点对空间复用信号进行放大转发,接收节点收到足够数量的样本即可译码,减少了发送时间,提高了传输的有效性。针对三节点数据交互的情形,分析了该技术的频谱效率。结果表明,在高信噪比区域,相对于传统的和基于协作分集的传输方案,该技术的频谱效率分别提升了100%和200%。     

双向AF中继系统中继选择及功率分配策略

为了提高双向中继系统的系统可达总速率,提出一种Two-way AF中继系统的最大化瞬时信噪比乘积（MISP）的中继选择策略,该策略联合考虑了两条链路的链路质量,实现了最优中继选择。在此基础上,运用信噪比平衡技术进行瞬时功率分配（SNR-Balancing-IPA）,推导出了最优功率分配方案的表达式。理论分析和仿真结果都表明,建议的最大瞬时信噪比乘积中继选择及SNR-Balancing-IPA的策略有效提高了系统的可达总速率,降低了系统的中断概率,改善了系统的性能。     

Adaptive algorithm for sparse signal recovery

The development of compressive sensing in recent years has given much attention to sparse signal recovery. In sparse signal recovery, spike and slab priors are playing a key role in inducing sparsity. The use of such priors, however, results in non-convex and mixed integer programming problems. Most of the existing algorithms to solve non-convex and mixed integer programming problems involve either simplifying assumptions, relaxations or high computational expenses. In this paper, we propose a new adaptive alternating direction method of multipliers (AADMM) algorithm to directly solve the suggested non-convex and mixed integer programming problem. The algorithm is based on the one-to-one mapping property of the support and non-zero element of the signal. At each step of the algorithm, we update the support by either adding an index to it or removing an index from it and use the alternating direction method of multipliers to recover the signal corresponding to the updated support. Moreover, as opposed to the competing “adaptive sparsity matching pursuit” and “alternating direction method of multipliers” methods our algorithm can solve non-convex problems directly. Experiments on synthetic data and real-world images demonstrated that the proposed AADMM algorithm provides superior performance and is computationally cheaper than the recently developed iterative convex refinement (ICR) and adaptive matching pursuit (AMP) algorithms.     

通信网络中基于协作中继重传策略

针对信息包在通信网络中多播传输进行了研究,提出通信网络中基于协作中继重传策略。当信息包直传失败时,源节点协作多个中继节点对多个丢失的信息包进行编码重传。在重传阶段根据反馈机制结果采取随机接入方式优先对有机会编码的丢失信息包进行组合,然后通过牺牲之前重传过程中传输失败的节点为信息提供空间分集增益,从而减低重传次数。最后在不同信道环境下,该策略与未协作NCARQ和传统ARQ进行Monte Carlo仿真。仿真结果表明,在多中继信道条件优于源-目的信道的情况下,利用协作网络编码进行重传有效地提高网络吞吐量,且该策略利用协作空间分集降低了由于相干性而导致性能不佳的状况。     

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。     

Tensor-based methods for blind spatial signature estimation under arbitrary and unknown source covariance structure

Spatial signature estimation is a problem encountered in several applications in signal processing such as mobile communications, sonar, radar, astronomy and seismology. In this paper, we propose higher-order tensor methods to solve the blind spatial signature estimation problem using planar arrays. By assuming that sources' powers vary between successive time blocks, we recast the spatial and spatiotemporal covariance models for the received data as third-order PARATUCK2 and fourth-order Tucker4 tensor decompositions, respectively. Firstly, by exploiting the multilinear algebraic structure of the proposed tensor models, new iterative algorithms are formulated to blindly estimate the spatial signatures. Secondly, in order to achieve a better spatial resolution, we propose an expanded form of spatial smoothing that returns extra spatial dimensions in comparison with the traditional approaches. Additionally, by exploiting the higher-order structure of the resulting expanded tensor model, a multilinear noise reduction preprocessing step is proposed via higher-order singular value decomposition. We show that the increase on the tensor order provides a more efficient denoising, and consequently a better performance compared to existing spatial smoothing techniques. Finally, a solution based on a multi-stage Khatri–Rao factorization procedure is incorporated as the final stage of our proposed estimators. Our results demonstrate that the proposed tensor methods yield more accurate spatial signature estimates than competing approaches while operating in a challenging scenario where the source covariance structure is unknown and arbitrary (non-diagonal), which is actually the case when sample covariances are computed from a limited number of snapshots.     

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.     

一种盲信号处理中的时域自适应滤波算法

滤波是信号处理中的重要环节,鉴于盲信号处理本身的特点,传统的滤波技术并不适合直接用于盲源分离之中。然而作为分离前的预处理,滤波技术在独立成分分离算法中是必要的。为此,本文结合稳健的数据非线性投影,首次提出盲信号中的自适应滤波方法,与此同时给出了具有自适应特性的阈值判决。在此基础上构造了盲信号中的自适应滤波算法,解决了利用低通和高通滤波处理盲信号所遇到的问题。仿真结果表明,在不破坏数据统计特性的前提下,该方法能有效滤除数据中的野值成分,避免了野值数据对独立成分分离算法性能的影响,为盲信号分离的预处理开辟了一种新的途径。     

基于三维曲波变换的弱信号恢复方法研究

传统的弱信号恢复方法在强噪声背景下具有较大的局限性,有用的信息往往淹没在强噪声背景下不易被识别。针对这个难点,提出一种基于三维曲波变换的弱信号恢复的方法。该方法将三维曲波变换和自适应滤波器相融合,从而提高数据中弱信号的能量,使得弱信号更易于被恢复。为了验证该方法的有效性,对楔形模型与实际三维数据进行处理。实验结果表明,恢复后的数据信噪比提高了2 dB到3 dB,频带也被拓宽了150 Hz,弱信号得到较好的恢复。     

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。