Optimizing random walk search algorithms in P2P networks

In this paper we develop a model for random walk-based search mechanisms in unstructured P2P networks. This model is used to obtain analytical expressions for the performance metrics of random walk search in terms of the popularity of the resource being searched for and the random walk parameters. We propose an equation-based adaptive search mechanism that uses an estimate of the popularity of a resource in order to choose the parameters of random walk such that a targeted performance level is achieved by the search. We also propose a low-overhead method for maintaining an estimate of popularity that utilizes feedback (or lack there-off) obtained from previous searches. Simulation results show that the performance of the equation-based adaptive search is significantly better than the non-adaptive random walk and other straight-forward adaptive mechanisms.     

交叉极化天线阵列信道的相关性

针对符合多簇拉普拉斯分布的功率角度谱,推导了适用于均匀线阵和均匀圆阵信道相关性的闭式表达式. 该表达式反映了信道角度功率谱以及交叉极化鉴别率对信道相关性的影响. 比较了具有相同角度扩展的单簇与多簇拉普拉斯角度功率谱引入信道相关性的差异. 分析了簇内角度扩展不同时,天线阵列信道相关性随均匀线阵天线间距或均匀圆阵半径的变化. 给出了簇内平均角和簇内角度扩展分别取不同值时,信道交叉极化鉴别率对应的等效天线间距.     

基于特征提取的缺陷图像分类方法

针对缺陷图像表面复杂多变、特征不宜提取的特点,提出了一种归一化转动惯量特征和不变矩特征相结合的时域分析方法来构建缺陷图像的统计特征量,同时增加缺陷矩形框区域内压缩度、距离极值比和线度特征量作为缺陷分类的依据;提出了在缺陷频谱图像内提取特征量的频域分析方法,并将矩形框区域内所有像素点灰度平均值和灰度方差值作为缺陷分类的另一重要依据;同时将BP神经网络应用于缺陷图像的自动分类中,构建了系统的缺陷分类器,并对现场采集的常见6种缺陷类型进行了实验.结果表明,该特征提取方法在很大程度上提高了特征的分类有效性;该BP分类器识别率较高,现场整体识别率达到90%以上,在一定程度上解决了缺陷图像分类难的问题.     

Kalman滤波预测的多小区子载波分配方法

小区间干扰协调是3G演进系统中抑制小区间干扰的重要策略, 并且在各类小区间干扰协调方案包含着不同的频率复用方法。为了在抑制干扰的同时提高频谱利用率,提出了可用频率集合的概念。如何分配可用频率集合中不同频点的子载波直接关系到小区间干扰的强弱程度,而现有的子载波分配方法往往基于当前信道状况, 导致在分配过程中因时间的滞后性而带来较大的干扰。针对这种情况,提出了一种基于Kalman滤波预测的自适应子载波分配方法。该方法通过检测获取不同子载波对用户的信道增益干扰比(GIR), 进而采用Kalman滤波估计得到下一时刻的GIR预测值大小,并以该预测值作为子载波分配的依据。仿真结果表明,与固定子载波分配方法和基于当前GIR的子载波分配方法相比,该方法能有效降低不同频率复用方法下的小区间干扰,改善小区和用户的性能,具体表现在提高了小区吞吐量,降低了用户的阻塞率,以及改善了小区边缘用户的比特丢失率。     

Multi-channel power allocation based on market competitive equilibrium in cognitive radio networks

Cognitive radios such as intelligent phones and Bluetooth devices have been considered essential goods in next-generation communication systems.Such devices will have to share the same frequency band owing to the limited bandwidth resource.To improve spectrum efciency,we formulate multi-channel power allocation as a market competitive equilibrium(CE)problem,and prove that its solution exists and is unique under the condition of weak interference.We then propose two distributed power allocation algorithms achieving CE,namely the fast convergent power allocation algorithm(FCPAA)and the social-fairness-aware FCPAA(SFAF).Theoretical analysis and simulation results demonstrate that the proposed algorithms lead to better system performance in terms of the guaranteed interference temperature constraint using the price mechanism instead of a strategy based on the Nash equilibrium.Moreover,it is shown that the FCPAA maximizes total utility,and converges more quickly than the method addressed in prior research with the help of improved round-robin rules.However,the FCPAA cannot ensure the same social fairness among secondary users as the SFAF scheme in both the non-fading channel and Rayleigh fading channel;the SFAF balances the individual utility by adjusting each user’s budget at the expense of a small quantity of system total throughput.     

A novel Parzen probabilistic neural network based noncoherent detection algorithm for distributed ultra-wideband sensors

Ultra-wideband (UWB) has been widely recommended for significant commercial and military applications. However, the well-derived coherent structures for UWB signal detection are either computationally complex or hardware impractical in the presence of the intensive multipath propagations. In this article, based on the nonparametric Parzen window estimator and the probabilistic neural networks, we suggest a low-complexity and noncoherent UWB detector in the context of distributed wireless sensor networks (WSNs). A novel characteristic spectrum is firstly developed through a sequence of blind signal transforms. Then, from a pattern recognition perspective, four features are extracted from it to fully exploit the inherent property of UWB multipath signals. The established feature space is further mapped into a two-dimensional plane by feature combination in order to simplify algorithm complexity. Consequently, UWB signal detection is formulated to recognize the received patterns in this formed 2-D feature plane. With the excellent capability of fast convergence and parallel implementation, the Parzen Probabilistic Neural Network (PPNN) is introduced to estimate a posteriori probability of the developed patterns. Based on the underlying Bayesian rule of PPNN, the asymptotical optimal decision bound is finally determined in the feature plane. Numerical simulations also validate the advantages of our proposed algorithm.     

Self-organizing adaptive fuzzy neural control for the synchronization of uncertain chaotic systems with random-varying parameters

This paper proposes a self-organizing adaptive fuzzy neural control (SAFNC) for the synchronization of uncertain chaotic systems with random-varying parameters. The proposed SAFNC system is composed of a computation controller and a robust controller. The computation controller containing a self-organizing fuzzy neural network (SOFNN) identifier is the principle controller. The SOFNN identifier is used to online estimate the compound uncertainties with the structure and parameter learning phases of fuzzy neural network (FNN), simultaneously. The structure-learning phase consists of the growing of membership functions, the splitting of fuzzy rules and the pruning of fuzzy rules, and thus the SOFNN identifier can avoid the time-consuming trial-and-error tuning procedure for determining the network structure of fuzzy neural network. The robust controller is used to attenuate the effects of the approximation error so that the synchronization of chaotic systems is achieved.All the parameter learning algorithms are derived based on the Lyapunov stability theorem to ensure network convergence as well as stable synchronization performance. To demonstrate the effectiveness of the proposed method, simulation results are illustrated in this paper.     

Mobility-Aware RSVP: A framework for improving the performance of multimedia services over wireless IP-based mobile networks

This paper proposes the Mobility-Aware Resource Reservation Protocol (MARSVP) in which mobility and QoS signaling are performed as a single functional block. The key concept of MARSVP is to convey mobility-specific information (binding updates and their associated acknowledgments) by using newly defined RSVP objects embedded in existing RSVP messages. An appealing feature of MARSVP is that it adheres to the current RSVP standard (RFC 2205) and thus requires minimal changes to end nodes without affecting any of the conventional RSVP routers in between. The proposed mechanism is evaluated using a simulation model for application-level performance and an analytical model for network-level signaling cost. Simulation results indicate a 27.9% improvement in QoS interruption when using Mobile IPv6 (MIPv6), 12.5% when using Hierarchical Mobile IPv6 (HMIPv6), and no improvement when using Fast Handovers for MIPv6 (FMIPv6). On the network-level, signaling cost savings of 9.4% and 11.9% are achieved for MIPv6 and HMIPv6, respectively, while FMIPv6 achieves savings of 17.9% when using Voice-over-IP traffic and 26.7% for Video-over-IP traffic. The results of the conducted studies indicate MARSVP’s superiority to conventional RSVP when deployed over wireless networks.     

Serviceaware proactive vertical handoff algorithm based on bandwidth requirement prediction

Due to the heterogeneity and versatility of emerging services and applications in wireless networks,it has been a great challenge to improve the system performance with both the awareness of service ch...     

Novel flow control mechanism based on improved BP neural network in cognitive packet network

In this paper, a novel flow control mechanism in cognitive packet network (CPN) based on the improved back propagation (BP) neural network is proposed, considering the flow distribution status predicted by BP neural network when packets are routed. The objective is to increase the capacity of CPN and improve the quality of service (QoS) by achieving flow balance. Besides, considering the slow convergence speed of traditional BP algorithm and the quick change of the flow status in cognitive packet network, an improved BP algorithm with dynamic learning rate is designed in order to achieve a higher convergence speed. The mechanism, which we propose, regards the predicated traffic data as an important factor when packets are routed to implement flow control. By achieving balance, the quality of network can be improved obviously. The simulation results show that the proposed mechanism provides better average time delay and packets loss ratio.