Stochastic radio channel model for advanced indoor mobile communication systems

Heddergott, R.; Bernhard, U.P.; Fleury, Bernard Henri

doi:10.1109/pimrc.1997.624379

Cited by 43 publications

(23 citation statements)

References 8 publications

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“…The parameters of a DCIR can be defined by the specification of a delay-angle distribution for the incident waves [39], [40], or of IO configurations [41]. In the sequel, both approaches and a transformation between PDDPs resulting from the specification of a delay-angle distribution and IO geometry are briefly sketched.…”

Section: A the Generation Of Cirsmentioning

confidence: 99%

The COST259 Directional Channel Model-Part I: Overview and Methodology

Molisch

Asplund

Heddergott

et al. 2006

IEEE Trans. Wireless Commun.

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186

116

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Abstract-This paper describes a model for mobile radio channels that includes consideration of directions of arrival and is thus suitable for simulations of the performance of wireless systems that use smart antennas. The model is specified for 13 different types of environments, covering macro-micro-and picocells. In this paper, a hierarchy of modeling concepts is described, as well as implementation aspects that are valid for all environments. The model is based on the specification of directional channel impulse response functions, from which the impulse response functions at all antenna elements can be obtained. A layered approach, which distinguishes between external (fixed), large-scale-, and small-scale-parameters allows an efficient parameterization. Different implementation methods, based on either a tapped-delay line or a geometrical model, are described. The paper also derives the transformation between those two approaches. Finally, the concepts of clusters and visibility regions are used to account for large delay and angular spreads that have been measured. In two companion papers, the environment-specific values of the model parameters are explained and justified.Index Terms-Direction of arrival, mobile radio channel, smart antennas.

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Section: A the Generation Of Cirsmentioning

confidence: 99%

The COST259 Directional Channel Model-Part I: Overview and Methodology

Molisch

Asplund

Heddergott

et al. 2006

IEEE Trans. Wireless Commun.

Self Cite

186

116

View full text Add to dashboard Cite

show abstract

“…Early research that specifically addressed the angle of arrival is found in [13]- [15] and [16]. More recently, research has begun to focus on statistical channel models that include both time and angle of arrival [17], [18], though as yet no space-time channel models based on measured channel responses have been developed.…”

Section: Introductionmentioning

confidence: 99%

Modeling the statistical time and angle of arrival characteristics of an indoor multipath channel

Spencer

Jeffs

Jensen

et al. 2000

IEEE J. Select. Areas Commun.

598

384

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Abstract-Most previously proposed statistical models for the indoor multipath channel include only time-of-arrival characteristics. However, in order to use statistical models in simulating or analyzing the performance of systems employing spatial diversity combining, information about angle of arrival statistics is also required. Ideally, it would be desirable to characterize the full space-time nature of the channel. In this paper, a system is described that was used to collect simultaneous time and angle of arrival data at 7 GHz. Data processing methods are outlined, and results obtained from data taken in two different buildings are presented. Based on the results, a model is proposed that employs the clustered "double Poisson" time-of-arrival model proposed by Saleh and Valenzuela (1987). The observed angular distribution is also clustered with uniformly distributed clusters and arrivals within clusters that have a Laplacian distribution.

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“…However, the assumptions are not realistic in practice. For example, studies [6], [17] have claimed that it is better to use the semi-Markov process, rather than a timehomogeneous Markov chain, for protocol evaluation in indoor environments with frequency-selective channels. Therefore, the nonstationary stochastic control is applied in TAEC to cope with the inevitable modeling error and nonstationary parameter variation problem.…”

Section: Related Workmentioning

confidence: 99%

Type-Aware Error Control for Robust Interactive Video Services over Time-Varying Wireless Channels

Liao

Lai²

2011

IEEE Trans. on Mobile Comput.

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Abstract-We propose a scheme called Type-Aware Error Control (TAEC) to provide robust interactive video services over timevarying wireless channels. Basically, TAEC explores the temporal dependence of video frames by pushing out the lower impact frames. In doing so, queuing delay is controlled and the important frame is retransmitted multiple times. We consider the application of the nonstationary stochastic control to further improve the wireless system throughput by exploring the multiuser diversity gain. The techniques involved in our proposed solution consist of constructing a Markov model, reducing the number of parameters estimated, and avoiding the state explosion problem. The provisioning of immediate bandwidth guarantee is another feature of TAEC, i.e., when exploring the multiuser diversity gain, the users share their reserved bandwidth only if the perceived video qualities are acceptable. As shown in the simulation, TAEC improves the perceived video quality in terms of luminance PSNR when compared to other schemes.

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Stochastic radio channel model for advanced indoor mobile communication systems

Cited by 43 publications

References 8 publications

The COST259 Directional Channel Model-Part I: Overview and Methodology

The COST259 Directional Channel Model-Part I: Overview and Methodology

Modeling the statistical time and angle of arrival characteristics of an indoor multipath channel

Type-Aware Error Control for Robust Interactive Video Services over Time-Varying Wireless Channels

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