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Journal ArticleDOI

Channel parameter estimation in mobile radio environments using the SAGE algorithm

01 Mar 1999-IEEE Journal on Selected Areas in Communications (IEEE)-Vol. 17, Iss: 3, pp 434-450
TL;DR: The investigations demonstrate that the SAGE algorithm is a powerful high-resolution tool that can be successfully applied for parameter extraction from extensive channel measurement data, especially for the purpose of channel modeling.
Abstract: This study investigates the application potential of the SAGE (space-alternating generalized expectation-maximization) algorithm to jointly estimate the relative delay, incidence azimuth, Doppler frequency, and complex amplitude of impinging waves in mobile radio environments The performance, ie, high-resolution ability, accuracy, and convergence rate of the scheme, is assessed in synthetic and real macro- and pico-cellular channels The results indicate that the scheme overcomes the resolution limitation inherent to classical techniques like the Fourier or beam-forming methods In particular, it is shown that waves which exhibit an arbitrarily small difference in azimuth can be easily separated as long as their delays or Doppler frequencies differ by a fraction of the intrinsic resolution of the measurement equipment Two waves are claimed to be separated when the mean-squared estimation errors (MSEEs) of the estimates of their parameters are close to the corresponding Cramer-Rao lower bounds (CRLBs) derived in a scenario where only a single wave is impinging The adverb easily means that the MSEEs rapidly approach the CLRBs, ie, within less than 20 iteration cycles Convergence of the log-likelihood sequence is achieved after approximately ten iteration cycles when the scheme is applied in real channels In this use, the estimated dominant waves can be related to a scatterer/reflector in the propagation environment The investigations demonstrate that the SAGE algorithm is a powerful high-resolution tool that can be successfully applied for parameter extraction from extensive channel measurement data, especially for the purpose of channel modeling
Citations
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Journal ArticleDOI
Andreas F. Molisch1
TL;DR: It is demonstrated how the frequency selectivity of propagation processes causes fundamental differences between UWB channels and "conventional" (narrowband) channels.
Abstract: This paper presents an overview of ultrawideband (UWB) propagation channels. It first demonstrates how the frequency selectivity of propagation processes causes fundamental differences between UWB channels and "conventional" (narrowband) channels. The concept of pathloss has to be modified, and the well-known WSSUS model is not applicable anymore. The paper also describes deterministic and stochastic models for UWB channels, identifies the key parameters for the description of delay dispersion, attenuation, and directional characterization, and surveys the typical parameter values that have been measured. Measurement techniques and methods for extracting model parameters are also different in UWB channels; for example, the concepts of narrowband channel parameter estimation (e.g., maximum-likelihood estimation) have to be modified. Finally, channel models also have an important impact on the performance evaluation of various UWB systems.

786 citations


Cites methods from "Channel parameter estimation in mob..."

  • ...2) The SAGE algorithm allows an iterative determination of the maximum-likelihood estimate of the parameters of the MPCs [75]....

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Journal ArticleDOI
TL;DR: It is found that in typical urban environments the power azimuth spectrum (PAS) is accurately described by a Laplacian function, while a Gaussian PDF matches the Azimuth PDF.
Abstract: A simple statistical model of azimuthal and temporal dispersion in mobile radio channels is proposed. The model includes the probability density function (PDF) of the delay and azimuth of the impinging waves as well as their expected power conditioned on the delay and azimuth. The statistical properties are extracted from macrocellular measurements conducted in a variety of urban environments. It is found that in typical urban environments the power azimuth spectrum (PAS) is accurately described by a Laplacian function, while a Gaussian PDF matches the azimuth PDF. Moreover, the power delay spectrum (PDS) and the delay PDF are accurately modeled by an exponential decaying function. In bad urban environments, channel dispersion is better characterized by a multicluster model, where the PAS and PDS are modeled as a sum of Laplacian functions and exponential decaying functions, respectively.

647 citations


Cites methods from "Channel parameter estimation in mob..."

  • ...The application of the SAGE algorithm for channel estimation is outlined in [20], [21]....

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Journal ArticleDOI
TL;DR: It turns out that the wideband double-directional evaluation is a most complete method for separating multipath components and is the important parameter for the capacity of multiple-input multiple-output (MIMO) channels.
Abstract: We introduce the concept of the double-directional mobile radio channel. It is called this because it includes angular information at both link ends, e.g., at the base station and at the mobile station. We show that this angular information can be obtained with synchronized antenna arrays at both link ends. In wideband high-resolution measurements, we use a switched linear array at the receiver and a virtual-cross array at the transmitter. We evaluate the raw measurement data with a technique that alternately used estimation and beamforming, and that relied on ESPRIT (estimation of signal parameters via rotational invariance techniques) to obtain superresolution in both angular domains and in the delay domain. In sample microcellular scenarios (open and closed courtyard, line-of-sight and obstructed line-of-sight), up to 50 individual propagation paths are determined. The major multipath components are matched precisely to the physical environment by geometrical considerations. Up to three reflection/scattering points per propagation path are identified and localized, lending insight into the multipath spreading properties in a microcell. The extracted multipath parameters allow unambiguous scatterer identification and channel characterization, independently of a specific antenna, its configuration (single/array), and its pattern. The measurement results demonstrate a considerable amount of power being carried via multiply reflected components, thus suggesting revisiting the popular single-bounce propagation models. It turns out that the wideband double-directional evaluation is a most complete method for separating multipath components. Due to its excellent spatial resolution, the double-directional concept provides accurate estimates of the channel's multipath-richness, which is the important parameter for the capacity of multiple-input multiple-output (MIMO) channels.

565 citations

Journal ArticleDOI
TL;DR: A 3GPP-like stochastic IR channel model is developed from measured power delay profiles, angle of departure, and angle of arrival power spectra, supporting air interface design of mmWave transceivers, filters, and multi-element antenna arrays.
Abstract: This paper presents a 3-D statistical channel impulse response (IR) model for urban line of sight (LOS) and non-LOS channels developed from 28- and 73-GHz ultrawideband propagation measurements in New York City, useful in the design of 5G wireless systems that will operate in both the ultra-high frequency/microwave and millimeter-wave (mmWave) spectrum to increase channel capacities. A 3GPP-like stochastic IR channel model is developed from measured power delay profiles, angle of departure, and angle of arrival power spectra. The extracted statistics are used to implement a channel model and simulator capable of generating 3-D mmWave temporal and spatial channel parameters for arbitrary mmWave carrier frequency, signal bandwidth, and antenna beamwidth. The model presented here faithfully reproduces realistic IRs of measured urban channels, supporting air interface design of mmWave transceivers, filters, and multi-element antenna arrays.

564 citations


Cites methods from "Channel parameter estimation in mob..."

  • ...…statistical simulator developed from 28-GHz wideband propagation measurements [40], and showed orders of magnitude increase in data rates compared with current 3G and 4G LTE mobile systems when using spatial multiplexing and beamforming at the base station (BS) for LOS and NLOS urban environments....

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Journal ArticleDOI
TL;DR: The investigation shows that the measured channels, for both array types, allow us to achieve performance close to that in i.i.d. Rayleigh channels, and concludes that in real propagation environments the authors have characteristics that can allow for efficient use of massive MIMO.
Abstract: Massive MIMO, also known as very-large MIMO or large-scale antenna systems, is a new technique that potentially can offer large network capacities in multi-user scenarios. With a massive MIMO system, we consider the case where a base station equipped with a large number of antenna elements simultaneously serves multiple single-antenna users in the same time-frequency resource. So far, investigations are mostly based on theoretical channels with independent and identically distributed (i.i.d.) complex Gaussian coefficients, i.e., i.i.d. Rayleigh channels. Here, we investigate how massive MIMO performs in channels measured in real propagation environments. Channel measurements were performed at 2.6 GHz using a virtual uniform linear array (ULA), which has a physically large aperture, and a practical uniform cylindrical array (UCA), which is more compact in size, both having 128 antenna ports. Based on measurement data, we illustrate channel behavior of massive MIMO in three representative propagation conditions, and evaluate the corresponding performance. The investigation shows that the measured channels, for both array types, allow us to achieve performance close to that in i.i.d. Rayleigh channels. It is concluded that in real propagation environments we have characteristics that can allow for efficient use of massive MIMO, i.e., the theoretical advantages of this new technology can also be harvested in real channels.

505 citations


Cites methods from "Channel parameter estimation in mob..."

  • ...The directional estimates for the ULA are obtained through the space-alternating generalized expectation maximization (SAGE) algorithm [31], which jointly estimates the delay, incidence azimuth, and complex amplitude, of multipath components (MPCs) in radio channels....

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References
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Journal ArticleDOI
TL;DR: In this article, a description of the multiple signal classification (MUSIC) algorithm, which provides asymptotically unbiased estimates of 1) number of incident wavefronts present; 2) directions of arrival (DOA) (or emitter locations); 3) strengths and cross correlations among the incident waveforms; 4) noise/interference strength.
Abstract: Processing the signals received on an array of sensors for the location of the emitter is of great enough interest to have been treated under many special case assumptions. The general problem considers sensors with arbitrary locations and arbitrary directional characteristics (gain/phase/polarization) in a noise/interference environment of arbitrary covariance matrix. This report is concerned first with the multiple emitter aspect of this problem and second with the generality of solution. A description is given of the multiple signal classification (MUSIC) algorithm, which provides asymptotically unbiased estimates of 1) number of incident wavefronts present; 2) directions of arrival (DOA) (or emitter locations); 3) strengths and cross correlations among the incident waveforms; 4) noise/interference strength. Examples and comparisons with methods based on maximum likelihood (ML) and maximum entropy (ME), as well as conventional beamforming are included. An example of its use as a multiple frequency estimator operating on time series is included.

12,446 citations

Journal ArticleDOI
TL;DR: Although discussed in the context of direction-of-arrival estimation, ESPRIT can be applied to a wide variety of problems including accurate detection and estimation of sinusoids in noise.
Abstract: An approach to the general problem of signal parameter estimation is described. The algorithm differs from its predecessor in that a total least-squares rather than a standard least-squares criterion is used. Although discussed in the context of direction-of-arrival estimation, ESPRIT can be applied to a wide variety of problems including accurate detection and estimation of sinusoids in noise. It exploits an underlying rotational invariance among signal subspaces induced by an array of sensors with a translational invariance structure. The technique, when applicable, manifests significant performance and computational advantages over previous algorithms such as MEM, Capon's MLM, and MUSIC. >

6,273 citations


"Channel parameter estimation in mob..." refers methods in this paper

  • ...The ESPRIT (estimation of signal parameter via rotational invariance techniques) [3] and Unitary ESPRIT [4] methods belong to the PSBE techniques....

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Book
15 Nov 1996
TL;DR: The EM Algorithm and Extensions describes the formulation of the EM algorithm, details its methodology, discusses its implementation, and illustrates applications in many statistical contexts, opening the door to the tremendous potential of this remarkably versatile statistical tool.
Abstract: The first unified account of the theory, methodology, and applications of the EM algorithm and its extensionsSince its inception in 1977, the Expectation-Maximization (EM) algorithm has been the subject of intense scrutiny, dozens of applications, numerous extensions, and thousands of publications. The algorithm and its extensions are now standard tools applied to incomplete data problems in virtually every field in which statistical methods are used. Until now, however, no single source offered a complete and unified treatment of the subject.The EM Algorithm and Extensions describes the formulation of the EM algorithm, details its methodology, discusses its implementation, and illustrates applications in many statistical contexts. Employing numerous examples, Geoffrey McLachlan and Thriyambakam Krishnan examine applications both in evidently incomplete data situations-where data are missing, distributions are truncated, or observations are censored or grouped-and in a broad variety of situations in which incompleteness is neither natural nor evident. They point out the algorithm's shortcomings and explain how these are addressed in the various extensions.Areas of application discussed include: Regression Medical imaging Categorical data analysis Finite mixture analysis Factor analysis Robust statistical modeling Variance-components estimation Survival analysis Repeated-measures designs For theoreticians, practitioners, and graduate students in statistics as well as researchers in the social and physical sciences, The EM Algorithm and Extensions opens the door to the tremendous potential of this remarkably versatile statistical tool.

5,998 citations


"Channel parameter estimation in mob..." refers background or methods in this paper

  • ...The EM algorithm [15], [16], has been formulated by Dempsteret al....

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  • ...Moreover the sequence converges to a stationary point provided the iteration function which is needed to compute (9) satisfies some weak regularity conditions [15]....

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Journal ArticleDOI
TL;DR: The article consists of background material and of the basic problem formulation, and introduces spectral-based algorithmic solutions to the signal parameter estimation problem and contrast these suboptimal solutions to parametric methods.
Abstract: The quintessential goal of sensor array signal processing is the estimation of parameters by fusing temporal and spatial information, captured via sampling a wavefield with a set of judiciously placed antenna sensors. The wavefield is assumed to be generated by a finite number of emitters, and contains information about signal parameters characterizing the emitters. A review of the area of array processing is given. The focus is on parameter estimation methods, and many relevant problems are only briefly mentioned. We emphasize the relatively more recent subspace-based methods in relation to beamforming. The article consists of background material and of the basic problem formulation. Then we introduce spectral-based algorithmic solutions to the signal parameter estimation problem. We contrast these suboptimal solutions to parametric methods. Techniques derived from maximum likelihood principles as well as geometric arguments are covered. Later, a number of more specialized research topics are briefly reviewed. Then, we look at a number of real-world problems for which sensor array processing methods have been applied. We also include an example with real experimental data involving closely spaced emitters and highly correlated signals, as well as a manufacturing application example.

4,410 citations


"Channel parameter estimation in mob..." refers background in this paper

  • ...These methods can be grouped into three of the categories defined in [ 1 ]: spectral estimation, parametric subspace-based estimation (PSBE), and deterministic parametric estimation (DPE)....

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