一种适用于非城市区域的卫星移动通信统计模型

本文提出了一种适用于非城市区域的卫星移动信道统计模型－莱斯－K模型，采用K－分布来描述由于遮蔽引起的接收信号包络的缓慢变化，用莱斯分布来描述包含直视信号分量（主径）在内的多径快衰落，得到了接收信号包络的概率分布和误比特率。在此基础上，本文利用样本矩对莱斯－K模型参数进行了估计并对参数估计性能进行了评价。最后，本文利用实测数据对模型进行了验证。     

降雨环境中多径信道的包络概率密度函数

 利用随机介质中波传播理论研究了随机雨介质中信号的起伏特性,研究了降雨散射环境对多径信道中接收信号包络概率密度函数的影响,导出了降雨环境下单波和双波模型的计算公式,讨论了评估降雨环境中的莱斯衰落分布和瑞利衰落分布多径信道概率密度函数的研究方法.文中的研究方法对于雪、沙尘等环境中多径信道的信号包络概率密度函数也适用.     

空地通信信道的统计特性研究

空地通信信道的主要统计特性包括多径衰落特性、多普勒功率谱和时延功率谱。将空地通信信道视为“双线模型”,推导了用信号平均接收功率和莱斯因子表示的接收信号包络的概率密度函数;假设接收端多径分量的到达角服从某范围内高斯分布,给出了接收信号的多普勒功率谱表达式及其示意图;考虑地球曲率对空地通信的影响,建立了信道的时延模型,并给出了接收信号的时延功率谱。     

频率选择性慢衰落信道下的一种Rake接收技术

通过对无线通信信道传输特性的讨论,以瑞利信道和莱斯信道为模型,研究在QPSK扩频调制下接收端信号的同步、多径分离与按增益合并.仿真实现了频率选择性慢衰落信道下的一种Rake接收技术,结果表明用他来减小多径衰落是有效的.     

无线移动信道的仿真

本文介绍了利用MATLAB来对无线移动信道进行仿真的方法.首先解释移动信号多径传播和因此造成的衰落的基本特性,然后重点对常见的瑞利衰落和莱斯衰落进行仿真.为了对衰落有个直观的表达,在仿真衰落的同时也给出了衰落幅度的分布或概率密度函数.     

莱斯因子估计在测向误差分析中的应用

在实际应用中,影响测向精度的重要因素是由于多径效应引起的测向误差。在能够接收到直射波时的莱斯信道条件下,表征其中多径信号影响程度的一个重要参数是莱斯因子。探讨了不同莱斯因子时测向系统接收信号的相位和测向精度受到的影响,给出了不同莱斯因子条件下测向精度的CRAMER-RAO界,总结了计算莱斯因子的可行方法。通过估计莱斯因子,可以对测向结果的可信度进行估计。     

基于镜面反射模型的飞行器间数据链多径效应研究

为了探究多飞行器间的多径效应对其通信数据链的影响,优化多飞行器间的通信,文中以镜面反射模型为基础,建立镜面反射多径效应模型,并分析计算接收天线的功率。在此基础上,建立多飞行器间通信数据链多径模型,引进飞行器机身反射波,并计算接收机功率。通过莱斯因子与误码率、信噪比的关系得出低莱斯因子信号在低误码率要求下需要满足更大的E_b/B_0。最后,提出通过扩频调制来实现信号的抗多径衰弱能力。     

无线移动信道的仿真

本文介绍了利用MATLAB来对无线移动信道进行仿真的方法。首先解释移动信号多径传播和因此造成的衰落的基本特性，然后重点对常见的瑞利衰落和莱斯衰落进行仿真；为了对衰落有个直观的表达，在仿真衰落的同时也给出了衰落幅度的分布或概率密度函数。     

OFDM-MIMO雷达低空目标探测性能研究

针对低空多径效应和地杂波复合作用导致的低空目标探测性能下降, 利用正交频分复用(Orthogonal Frequency Division Multiplexing, OFDM)-多输入多输出(Multiple-Input Multiple-Output, MIMO)雷达的空间和频率分集特性提高低空目标探测性能.在建立正交频分复用-线性调频发射信号模型、多径效应模型和基于对称阿尔法稳定分布的地杂波模型基础上, 得出了OFDM-MIMO雷达接收信号模型.采用匹配滤波器恢复发射-接收天线对信号, 通过接收信号融合实现参差补偿, 减小了接收信号能量的深度衰减和剧烈起伏.仿真算例表明OFDM-MIMO雷达能够有效减小回波信号剧烈起伏和杂波对目标探测的影响, 实现多径干扰和杂波背景下对低空目标的稳定探测.     

多径衰落信道的分数布朗运动模型

本文旨在将分形理论引入多径衰落信道的研究,从非线性科学的角度出发建立了多径信道的分数布朗运动模型.通过探讨多径信号的增量统计分布、统计自相似性、广义功率谱和分形维数,揭示了多径衰落与分数布朗运动间的内在联系;指出信号的分维是描述无线信道传播特性的重要参数;在分形模型的基础上,对多径信号进行了重构.实验结果表明,无线信道的分数布朗运动模型比传统的随机模型能更有效地刻划和描述多径衰落的行为.     

Performance Analysis of System with Micro- and Macrodiversity Reception in Correlated Gamma Shadowed Rician Fading Channels

In this paper, the performance of wireless system employing microdiversity to mitigate the effects of short-term fading and macrodiversity to reduce long-term fading (shadowing) effects is studied. The system model assumes implementation of maximal-ratio combining (MRC) at the microlevel and selection combining (SC) at the macrolevel. The received signal envelope follows a Rician distribution and it also suffers gamma shadowing. Novel expressions for the probability density function (PDF), cumulative distribution function (CDF), and moment-generating function (MGF) of the output signal-to-noise ratio (SNR) are obtained. Several useful performance criteria, such as the moments of the output SNR and outage probability are analytically derived. Moreover, the average bit error probability (ABEP) for noncoherent binary differential phase-shift keying (BDPSK) is calculated using the MGF based approach while the ABEP for coherent binary phase-shift keying (BPSK) is studied by averaging the conditional bit error probability over the PDF. Numerical results are graphically presented to show the effects of various system parameters to the system performance, as well as the enhancement due to use of the combination of micro- and macrodiversity. Some of numerical results are complemented by equivalent computer simulated results which validate the accuracy of the proposed analysis. The agreement between the Rician-gamma and Rician-lognormal fading model is also established.     

Approximate Outage Analysis of Land Mobile Satellite Systems in Lognormally Shadowed Rician Channels

Among the proposed models for land mobile satellite (LMS) channels, the shadowed Rice model proposed originally by Loo, has found wide applications in different frequency bands. In Loo’s model, it is assumed that the received signal is affected by nonselective Rice fading with lognormal shadowing on the direct component only, while the diffuse scattered component has constant average power level. The resulting composite probability density function (PDF) includes an infinite-range integral and is not available in closed-form, thereby making the performance evaluation of LMS communication links in these channels cumbersome. To bypass this problem, in this paper an approximation method is developed which makes it possible to describe the envelope PDF as a sum of weighted Rice’s PDFs. Therefore, in contrast with Loo’s PDF, the proposed method leads to an easy-to-use, closed-form approximate expression for the PDF and also for the most statistical characteristics, such as cumulative distribution function and moments of the signal envelop in shadowed Rice channels. Based on the derived expressions, the performance analysis of a single receiver operating over lognormally shadowed Rice channel is investigated in terms of the outage probability. Numerically evaluated results show the good accuracy of the proposed approximation method.     

A general statistical channel model for mobile satellite systems

In order to guarantee the service quality and reliability for mobile satellite communication systems, we have to take into account outages due to obstruction of the line-of-sight path between a satellite and a mobile terminal as well as the signal fluctuation caused by interference from multipath radio waves. Thus, we need a good characterization for the satellite propagation channel. In this paper, we derive a general statistical model for mobile communication systems based on propagation scattering theory. Both the probability density function (PDF) of the received signal envelope and that of received signal power are given. This model is shown to be more general than previously known statistical models and can provide a better fit to the experimental data. This new model will play a significant role in the design of mobile satellite communication systems     

Recursive least-squares quadratic smoothing from measurements with packet dropouts

The least-squares quadratic filtering and fixed-point smoothing problems of discrete-time stochastic signals from observations with multiple packet dropouts are addressed. It is assumed that the packet dropouts occur randomly and the latest measurement received successfully is processed for the estimation in case that the current measurement is dropped-out. This situation is modelled by introducing in the observation model a sequence of Bernoulli random variables whose values - one or zero - indicate if the current measurement is received or dropped-out, respectively, and whose probability distributions are known. A recursive estimation algorithm is deduced without requiring full knowledge of the state-space model generating the signal process, but only information about the dropout probabilities and the moments of the signal and noise processes involved. Defining a suitable augmented observation model, the quadratic estimation problem is reduced to the linear estimation problem based on the augmented observations, which is solved by using an innovation approach.     

Signal Design for Low-Error Probability in Fading Dispersive Channels

The signal design problem for FSK communication via fading dispersive channels is considered. The channel is modeled as a linear filter whose time-varying impulse response is a sample function from a zero-mean Gaussian random field of arbitrary WSSUS type. The additive noise component in the received waveforms is supposed to be a zero-mean white Gaussian random process, and maximum likelihood demodulation is assumed. The signal design procedure here adopted consists of minimizing a known upper bound on the error probability, whereas the previous similar design method by Daly intended maximizing an upper bound on the detection probability for radar-astronomy targets. Though with slightly different optimal numerical values, here, as in Daly's problem, the signal design depends on a single parameter which is a simple functional of the channel timefrequency covariance function and of the signal envelope ambiguity function. A detailed example shows how the results of this concise paper can be used to optimize signal parameters and to predict the performance loss due to nonoptimal signal envelopes.     

Adaptive error coding using channel prediction

In this paper, we construct a finite-state Markov chain model for a Rayleigh fading channel by partitioning the range of the received signal envelope into K intervals. Using a simulation of the classic two-ray Rayleigh fading model, a Markov transition probability matrix is obtained. Using this matrix to predict the channel state, we introduce an adaptive forward error correction (FEC) coding scheme. Simulation results are presented to show that the adaptive FEC coding scheme significantly improves the performance of a wireless communication system.     

Statistical Characterization of Fading in LOS Wireless Channels with a Finite Number of Dominant Paths. Application in Millimeter Frequencies

Propagation Line Of Sight (LOS) indoor environment is studied through a new statistical model for the fast fading statistics which is based on the assumption of a small number of scatterrers. The analytical expression for the probability density function of fast fading statistics is presented and is applied in a mixture probability density function for the received signal envelope in order to incorporate shadowing. The new models are validated by measurements which have been conducted at NTUA in three different frequency bands, 1.8 GHz, 30 GHz and 60 GHz, in corridors. Finally the new models are compared with the traditional Rice and Rice-Lognormal respectively.     

Performance analysis of lognormally shadowed generalized Gamma fading channels

A new composite channel model is proposed for the performance analysis of shadowed fading channels. This model is represented as a mixture of generalized Gamma (GG) multipath fading and lognormal shadowing. GG distribution includes the Rayleigh, Nakagami, and Weibull as special cases; hence the presented model, which is referred to as GG‐L, is a generic model that covers many well‐known composite fading models, including the Rayleigh–lognormal (R‐L), Nakagami–lognormal (N‐L), and Weibull–lognormal (W‐L). The main drawback of the lognormal‐based composite models is that the composite probability density function (PDF) is not in closed form, thereby making the performance evaluation of communication links in these channels cumbersome. To bypass this problem, an approximation method is developed which makes it possible to derive a closed‐form, analytical expression for GG‐L composite distribution. The proposed method only needs the mean and the variance of the underlying lognormal distribution, and hence, bypasses the required complicated integration needed to calculate the PDF of the received signal envelope in GG‐L channel. Based on this method, the most statistical characteristics, such as cumulative density function (CDF) and moments of the GG‐L composite distribution, are derived and used for the performance analysis of a single receiver operating over GG‐L fading channel. Copyright © 2010 John Wiley & Sons, Ltd.     

Envelope detection of orthogonal signals with phase noise

The authors analyze the performance of receivers that use envelope detection at an IF to detect optical signals with orthogonal modulation formats. Exact closed-form expressions for the error probability conditioned on the normalized envelope were obtained. The only information necessary for obtaining the unconditional error probability is a small set of tilted moments of the envelope. The authors then provide an approximation to this envelope which is not only accurate to the first order in phase noise strength, but also has the same range as the actual random envelope. This approximation was used to obtain the bit error performance of the three receiver models considered. A tight lower bound in closed form is given. The analysis is extended to the case of N-ary frequency-shift-keying (FSK) to provide very tight upper and lower bounds to the bit error probability     

Recursive algorithms for identification of impulse noise channels

The Class A Middleton model is a widely accepted statistical-physical parameteric model for impulsive interference superimposed on a Gaussian background. In the present work, a recursive decision-directed estimator for online identification of the parameters of the Class A model is proposed. This estimator is based on an adaptive Bayesian classification of each of a sequence of Class A envelope samples as an impulsive sample or as a background sample. As each sample is so classified, recursive updates of the estimates of the second moment of the background component of the interference envelope density, the second moment of the impulsive component of the interference envelope density, and the probability with which the impulsive component occurs, are readily obtained. From these estimates, estimates of the parameters of the Class A model follow straightforwardly, since closed-form expressions for the parameters exist in terms of these quantities. The performance characteristics of this algorithm are investigated and an appropriately modified version is found to yield a recursive algorithm with excellent global performance