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.     

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.     

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.     

MIMO系统中利用空间相关性的同信道干扰消除算法

针对不适合进行波束赋型的MIMO系统中接收端如何消除同信道干扰的问题,提出了一种利用系统中干扰源的空间相关性进行同信道干扰消除的算法,并给出了估计空间相关性的最大似然算法。仿真结果表明,在2×2的Alamouti分集方案中,该算法相比于将干扰当作高斯白噪的方法在BER为10-2时可以获得3 dB的增益;在2×4的分层空时复用方案中,该算法在存在一个强干扰源的情况下优势明显。     

认知无线网络中基于微观经济学的动态频谱管理算法

该文提出了认知网络中基于微观经济学的动态频谱管理机制,实现异构无线网络中频谱资源的动态分配,从而最大化频谱资源的利用率并提高运营商的收益.文中引入了频谱资源的经济价值因素以保证运营商间频谱交易的合理性.此外,为了保证系统的性能,文中提出了一种基于地理信息系统的空分模型,并设计了干扰抑制技术以限制动态频谱管理带来的干扰.博弈论是一种能够有效解决自主分布式无线资源优化的算法,该文将其引入,为不同运营商提供双赢的频谱交易策略.仿真结果表明,该频谱管理机制提高了频谱利用率以及运营商的收益,并有效地抑制了系统间的干扰.     

Partial segmented compressed sampling for analog-to-information conversion

Recently, a segmented analog-to-information conversion (S-AIC) structure that can obtain more samples than the number of branches of mixers and integrators (BMIs) is proposed by Taheri and Vorobyov. To reduce the complexity of S-AIC, in this paper we propose a partial segmented AIC (PS-AIC) structure, where BMIs are divided into groups and each group only works within a partial period that is non-overlapping in time. The proposed PS-AIC offers an attractive tradeoff between the complexity and error performance. We also prove that the equivalent measurement matrix of PS-AIC satisfies the restricted isometry property. Simulations verify the effectiveness of PS-AIC.     

28 GHz下对毫米波透射特性的分析

毫米波段拥有高速传输数据的优点,是目前第5代(the 5th Generation,5G)移动通信系统的重要候选频段之一.为了研究毫米波的透射特性,将一对喇叭天线相向放置在玻璃门的两侧,在28 GHz频率下使用800 MHz带宽设备进行了一系列透射特性的测量.研究了不同入射角,极化方式及不同发射端与接收端(Transmitter-Receiver,Tx-Rx)距离对透射系数的影响.将测量结果与菲涅耳传播定律(包括光滑表面和提出的高斯粗糙表面模型)进行了对比.结果表明,文中提出的高斯粗糙表面模型与垂直和水平极化信号的透射特性更拟合.此外,实验也研究了收发端距离对透射的影响,并得出了不同入射角度下的穿透损耗值.     

xMax的类正弦波调制技术

通过对xMax技术指标的分析，阐述了实现xMax的方法。在此基础上提出了一种能实现xMax的类正弦波调制技术。该调制技术通过对类正弦波信号的单周期调制实现信号对信息的携带；并且能达到xMax主带宽窄、边带功率谱密度低、发射功率低、传输速率高等性能指标。通过理论分析和仿真验证，该调制技术功率谱主峰值带宽为300 kHz，边带功率谱密度比峰值功率谱低40 dB，使得边带能很好地隐藏在背景噪声中，传输速率达到7.5 Mbit/s。     

A Novel Dynamic Adjusting Algorithm for Load Balancing and Handover Co-Optimization in LTE SON

With the development of mobile internet and multi-media service, advanced techniques need to be applied in wireless network to improve user experience. Long term evolution (LTE) systems, which can offer up to 100Mbps downlink date rates, have been deployed in USA and Korea. However, because plenty of complex physical layer algorithms are utilized, network planning and optimization become heavy burdens for LTE network operators. Self-organizing network (SON) is a promising method to overcome this problem by automatically selecting and adjusting key parameters in LTE systems. In this paper, we present a dynamic adjusting algorithm to improve both handover and load balancing performance by introducing a weighted co-satisfaction factor (CSF). Analysis and system level simulation are conducted to exhibit the performance improvement of the proposed scheme. Results show that the proposed method outperforms the conventional solutions in terms of the network handover success ratio and load balancing gains significantly.     

Cooperative communications with hierarchical modulation in downlink OFDMA based cellular networks

Cooperative transmission is a promising way to improve system performance in orthogonal frequency division multiple access (OFDMA) based cellular networks.This paper proposes two heuristic cooperation ...