Semi-Markov multistate modeling of the land mobile propagation channel for geostationary satellites

This paper describes a new semi-Markov propagation channel model for land mobile satellite systems using geostationary satellites. The multistate model switches between propagation states representing line-of-sight, shadowing, or blockage of the signal. The duration of times spent in each state follow probability distributions recommended by the radiocommunication sector of the International Telecommunication Union (ITU-R). The actual parameters to be used with the ITU-R distributions were modified and fitted to observed data. The open-area state durations follow a power-law distribution, while the state durations for both the shadowed and the blocked states follow a lognormal distribution. Parameters for both two- and three-state models are extracted from an L-band measurement campaign performed by Inmarsat in the United Kingdom. Propagation channel models characterizing the fading within the open, shadowed, or blocked propagation states are described as well. The semi-Markov models represent an improvement over the more commonly used Markov models where the duration in each state follows an exponential distribution. The new model enables more accurate prediction and simulation of system performance and availability.     

Graph-Theoretic Complexity Reduction for Markovian Wireless Channel Models

Accurate simulation and analysis of wireless networks are inherently dependent on accurate models which are able to provide real-time channel characterization. High-order Markov chains are typically used to model errors and losses over wireless channels. However, complexity (i.e., the number of states) of a high-order Markov model increases exponentially with the memory-length of the underlying channel. In this paper, we present a novel graph-theoretic methodology that uses Hamiltonian circuits to reduce the complexity of a high-order Markov model to a desired state budget. We also demonstrate the implication of unused states in complexity reduction of higher order Markov model. Our trace-driven performance evaluations for real wireless local area network (WLAN) and wireless sensor network (WSN) channels demonstrate that the proposed Hamiltonian Model, while providing orders of magnitude reduction in complexity, renders an accuracy that is comparable to the Markov model and better than the existing reduced state models.     

Joint Density for Eigenvalues of Two Correlated Complex Wishart Matrices: Characterization of MIMO Systems

In this letter, the joint probability density function (PDF) for the eigenvalues of a complex Wishart matrix and a perturbed version of it are derived. The latter version can be used to model channel estimation errors and variations over time or frequency. As an example, the joint PDF is used to calculate the transition probabilities between modulation states in an adaptive MIMO system. This leads to a Markov model for the system. We then use the model to investigate the modulation state entering rates (MSER), the average stay duration (ASD), and the effects of feedback delay on the accuracy of modulation state selection in mobile radio systems. Other applications of this PDF are also discussed.     

Analysis of availability improvement in LMSS by means of satellitediversity based on three-state propagation channel model

Aiming at future multimedia land mobile-satellite services (LMSS) consisting of a large number of nongeostationary Earth-orbit satellites, we present an LMSS propagation channel model for assessing the effect of a satellite diversity scheme so that high service availability and high signal quality are assured. We classify general fading environments for LMSS into three states. By taking the occurrence probability of each state into account, a new fading channel model is developed. The validity of the model is identified by comparing its predicted values in terms of the cumulative distribution function (CDF) with measured data available so far. Then, based on this model, we calculate the satellite diversity effect assuming that the area is illuminated simultaneously by at least two satellites moving in low Earth orbits (LEO) over urban and suburban environments. In addition, state transition characteristics based on a Markov model are presented     

一种基于隐马尔可夫模型的IDS异常检测新方法

提出一种新的基于隐马尔可夫模型的异常检测方法,主要用于以shell命令或系统调用为原始数据的IDS。此方法对用户(或程序)行为建立特殊的隐马尔可夫模型,根据行为模式所对应的序列长度对其进行分类,将行为模式类型同隐马尔可夫模型的状态联系在一起,并引入一个附加状态。由于模型中各状态对应的观测值集合互不相交,模型训练中采用了运算量较小的的序列匹配方法,与传统的Baum-Welch算法相比,大大减小了训练时间。根据模型中状态的实际含义,采用了基于状态序列出现概率的判决准则。利用UNIX平台上用户shell命令数据进行的实验表明,此方法具有很高的检测准确性,其可操作性也优于同类方法。     

Interval-availability distribution of 2-state systems withexponential failures and phase-type repairs

Interval availability is a dependability measure defined as the fraction of time during which a system is in operation over a finite observation period. Usually, for computing systems, the models used to evaluate interval availability distribution are Markov models. Numerous papers using these models have been published, and only complex numerical methods have been proposed as solutions to this problem even in simple cases such as the 2-state Markov model. This paper proposes a new way to compute this distribution when the model is a 2-state semi-Markov process in which the holding times have an exponential distribution for the operational state and a phase-type distribution for the nonoperational one. The main contribution of this paper is to define a new algorithm to compute the interval availability distribution for systems having only one operational state. The computational complexity depends weakly on the number of states of the system, and sometimes it can deal also with infinite state spaces. Moreover, simple closed expressions of this distribution are shown when repair periods are of the Erlang type with eventually absorbing states     

Low Computational Complexity in Low-Cost GNSS Receivers

One of the key issues in low-cost GNSS receivers is the computational complexity. One of the computational components of the GNSS receivers is the satellite positioning calculations. The main focus of this paper is to reduce the computational burden of this stage of processing. In this paper, four different models to fit the GPS and GLONASS satellites orbit are investigated. These models are compared with each other in terms of their computational load and accuracy, and the models have good accuracy and less computational load are selected. Among these four methods, the Hermite model and the Chebyshev model are superior to other methods for determining orbits of GPS and GLONASS satellites, respectively. In order to evaluate the performance of these models, two different data including ground stations data and measured data by GNSS receivers are used. The results show that these two models can improve the computational burden by about 90% compared to conventional methods like the Runge–Kutta and the Keplerian parameters that used in GNSS receivers.

     

Adaptive coding modulation selection optimisation scheme for Ka-band LEO mobile satellites

To address the problem that Ka-band satellite communication signal transmission is easily affected by rainfall and terminal environment, combining the characteristics of high-speed movement of LEO satellites and the wave propagation characteristics of satellite-ground links, this paper establishes a Markov synthesis model of four-state satellite channels based on Ka-band that integrates rainfall attenuation and terminal shadow attenuation, and a scheme for adaptive coding and modulation selection based on the DVB-S2 standard is proposed. Based on this, a rainfall fading probability density function (PDF) based on the satellite elevation angle variation is derived, and a more efficient and streamlined set of modulation and coding(MODCOD) is obtained through simulations and calculations. The simulation results show that the proposed scheme not only effectively solves the problem of severe fading of the transmission signal due to rainfall, ground movement environment and satellite mobility but also significantly reduces the system complexity of the original DVB-S2 standard scheme with little loss of efficiency.     

基于马尔可夫过程的卫星移动信道模型及长期预测方法

卫星移动信道可被描述为基于有限状态马尔可夫过程的衰落模型,该文分析了卫星信道的可预测性,然后基于加权预测思想提出了一种卫星移动信道长期预测方法,该方法在当前信道采样的基础上进行二次采样,采样频率大于马尔可夫状态转移速率的2倍,利用信道状态的相关性和信道状态转移概率信息来加权预测未来长期内的信道状态,并依据自回归预测模型给出信道预测输出值,仿真结果表明,采用此方法对卫星信道未来的信道状态进行预测,在信噪比较高时均方误差能够达到10-2量级,在自适应传输过程中可以降低系统平均误比特率,且能够提高系统吞吐量性能,这对卫星移动通信系统的自适应传输和自适应资源分配都具有一定的指导意义。     

A novel approach to model the land mobile satellite channel through reversible jump markov chain monte carlo technique

A key issue in the design of a mobile satellite communication system is an adequate knowledge of the statistical behavior of the propagation channel. To achieve this goal, the development of very accurate models plays a very important role. In contrast to traditional multi-state Markov chain based models, the novel approach proposed in this paper makes no prior assumptions on the number of states or on the statistical distributions characterizing each state. The sequence of channel states is blindly estimated using a Reversible Jump Monte Carlo Markov Chain algorithm.     

Computationally efficient method for analyzing guard channel schemes

Call admission control (CAC) is important for cellular wireless networks in order to provide quality of service (QoS) requirements to users. Guard channel scheme is one of the CAC schemes. There are different computational models for analyzing the guard channel scheme which make unrealistic assumption of exponential distribution for both call holding duration and cell residence time for computational tractability. On the other hand, there are some more realistic models for guard channel schemes which capture general distributions of call holding duration and cell residence time by phase type distributions but are computationally cumbersome to implement. The state-spaces of the Markov chains for those models make the computation intractable. In this paper, we develop a tractable computational model to analyze guard channel scheme with general cell residence time and call holding duration captured by phase type distributions. We make our mathematical model computationally tractable by keeping track of the number of calls in different phases of the channel holding time instead of the phase of the channel holding time of individual calls.     

Satellite‐PCS channel simulation in mobile user environments using photogrammetry and Markov chains

In this paper, we present a system that models the land‐mobile satellite communication channel with a Markov chain approach supported by images of the mobile Earth station's (MES) environment. Our method employs a video camera with a fish‐eye lens capturing image sequences of the user environment from the position of the moving MES, image and signal processing algorithms to recognize propagation path states and extract their duration and transition probabilities, and modeling of the communication channel characteristics using the theory of Markov stochastic processes combined with statistical fade distributions appropriate for given path states. The results demonstrate that the procedure provides a full statistical characterization of the narrow‐band land‐mobile satellite propagation channel in several environments. This revised version was published online in June 2006 with corrections to the Cover Date.     

MIMO场景下的低轨卫星选星算法

低轨(LEO)宽带星座卫星通信作为地面5G无线通信系统的重要补充,始终面临地面可视卫星数量大、传统选星算法计算复杂度高等难题。为实现高效的卫星分组选择算法,基于多输入多输出(MIMO)系统原理,以大尺度路径损耗模型为基础,结合递减卫星选择算法,从而以较低计算复杂度、更快收敛速度有效逼近最优容量性能。该算法在典型LEO星座系统构型下通过数值仿真得到了验证,为未来5G低轨卫星星座通信传输方案设计提供了一种参考思路。     

Online Bayesian estimation of transition probabilities for Markovian jump systems

Markovian jump systems (MJSs) evolve in a jump-wise manner by switching among simpler models, according to a finite Markov chain, whose parameters are commonly assumed known. This paper addresses the problem of state estimation of MJS with unknown transition probability matrix (TPM) of the embedded Markov chain governing the jumps. Under the assumption of a time-invariant but random TPM, an approximate recursion for the TPMs posterior probability density function (PDF) within the Bayesian framework is obtained. Based on this recursion, four algorithms for online minimum mean-square error (MMSE) estimation of the TPM are derived. The first algorithm (for the case of a two-state Markov chain) computes the MMSE estimate exactly, if the likelihood of the TPM is linear in the transition probabilities. Its computational load is, however, increasing with the data length. To limit the computational cost, three alternative algorithms are further developed based on different approximation techniques-truncation of high order moments, quasi-Bayesian approximation, and numerical integration, respectively. The proposed TPM estimation is naturally incorporable into a typical online Bayesian estimation scheme for MJS [e.g., generalized pseudo-Bayesian (GPB) or interacting multiple model (IMM)]. Thus, adaptive versions of MJS state estimators with unknown TPM are provided. Simulation results of TPM-adaptive IMM algorithms for a system with failures and maneuvering target tracking are presented.     

Variable order Markov modelling for LEO mobile satellite channels

A finite state Markov channel (FSMC) model for a digital narrowband mobile LEO satellite channel is presented, which can be used to predict the time-varying behaviour of the channel and to adapt the transmission to the channel state. The model is developed with the help of the so-called context tree pruning (CTP) algorithm. Its effectiveness is demonstrated by comparing theoretical results concerning the digital channel with simulated results obtained from time-continuous channel models     

Novel Approach for Modeling Wireless Fading Channels Using a Finite State Markov Chain

Empirical modeling of wireless fading channels using common schemes such as autoregression and the finite state Markov chain (FSMC) is investigated. The conceptual background of both channel structures and the establishment of their mutual dependence in a confined manner are presented. The novel contribution lies in the proposal of a new approach for deriving the state transition probabilities borrowed from economic disciplines, which has not been studied so far with respect to the modeling of FSMC wireless fading channels. The proposed approach is based on equal portioning of the received signal‐to‐noise ratio, realized by using an alternative probability construction that was initially highlighted by Tauchen. The associated statistical procedure shows that a first‐order FSMC with a limited number of channel states can satisfactorily approximate fading. The computational overheads of the proposed technique are analyzed and proven to be less demanding compared to the conventional FSMC approach based on the level crossing rate. Simulations confirm the analytical results and promising performance of the new channel model based on the Tauchen approach without extra complexity costs.     

An adaptive receiver of joint data and channel estimation for meteor burst communications

In view of the characteristics of the meteor burst channel, the variable rate data transmission should be adopted to improve the system average throughput, which results in channel tracing and equalization problems at the receiver. Although the joint data and channel estimation of maximum likelihood sequence detection performing the principle of per‐survivor processing (PSP) is considered as an optimal detection scheme, its great computational complexity is a major problem and can hardly agree with the decreasing of the meteor channel. Based on the estimation of the system parameters, an adaptive state reduction of the PSP (ASRP) algorithm with only a few states in the trellis diagram is employed, while these states are chosen by the time‐varying threshold according to the exponential decay of meteor channels. It is shown that, ASRP can make a good tradeoff between the performance and the computational complexity, and provides reliable data transmission for adaptive modulation and coding of the meteor burst communication system. Computer simulation results and performance analysis are also included to support our developments. Copyright © 2010 John Wiley & Sons, Ltd.     

Efficient channel utilization for real-time video in OVSF-CDMA systems with QoS assurance

In this paper, the utilization of real-time video service in the downlink of an orthogonal variable spreading factor code division multiple access (OVSF-CDMA) system is studied. By modeling the video traffic and wireless channel as a joint Markov modulated process, and properly partitioning the states of the Markov process, an adaptive rate allocation scheme is proposed for real-time video transmission with quality of service provisioning while achieving high channel utilization. The scheme is applicable for packet switching and frame-by-frame real-time video transmission, and incorporates both the physical layer and network layer characteristics. For QoS provisions, the closed form expressions of packet delay and loss probability are derived based on the Markov model. Analytical and simulation results demonstrate that the proposed scheme can significantly improve the channel utilization over the commonly used effective bandwidth approach.     

Efficient dynamic channel allocation techniques with handoverqueuing for mobile satellite networks

Efficient dynamic channel allocation techniques with handover queuing suitable for applications in mobile satellite cellular networks, are discussed. The channel assignment on demand is performed on the basis of the evaluation of a suitable cost function. Geostationary and low Earth orbit (LEO) satellites have been considered. In order to highlight the better performance of the dynamic techniques proposed, a performance comparison with a classical fixed channel allocation (FCA) has been carried out, as regards the probability that a newly arriving call is not completely served. It has also been shown that a higher traffic density, with respect to GEO systems, is manageable by means of LEO satellites