Second-Order Spectral Statistics for the Power Gain of Wideband Wireless Channels

Derpich, Milan S.; Feick, R.

doi:10.1109/tvt.2013.2284241

Cited by 7 publications

(6 citation statements)

References 21 publications

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“…It is noticeable that this expression consists of two terms, the former being constant and the latter vanishing as the frequency difference increases. This feature is also observed in [8]. We obtain the PDP by applying the Fourier Transform on the autocorrelation,…”

Section: A Single Cluster Analysismentioning

confidence: 61%

Degrees of Freedom of Ray-Based Models for mm-Wave Wideband MIMO-OFDM

Roy

Paquelet

Crussière

2019

2019 IEEE Global Communications Conference (GLOBECOM)

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In this paper we analyze the number of degrees of freedom needed to accurately capture and model wideband MIMO-OFDM channels. Two modeling approaches are considered, either examining each coefficient of the conventional inputoutput MIMO channel matrix or investigating the components constituting the physical ray-based propagation between the antenna arrays. As our analysis accounts for wideband channels, a time domain decomposition of MIMO channels into tapped delay lines is performed in each case. The efficiency of each approach is then evaluated in terms of number of taps to determine the most appropriate representation between either the conventional antenna-based one or the propagation-based one. This analysis is performed analytically using the Saleh-Valenzuela model which is recognized for its consistency for wideband as well as spatial modeling. We exploit the second-order statistics of the Channel Impulse Response to derive the taps weights.

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Section: A Single Cluster Analysismentioning

confidence: 61%

Degrees of Freedom of Ray-Based Models for mm-Wave Wideband MIMO-OFDM

Roy

Paquelet

Crussière

2019

2019 IEEE Global Communications Conference (GLOBECOM)

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“…the auto-correlation function of the squared magnitudes of the transfer function. The power autocorrelation function for the Saleh-Velenzuela model was previously studied in [40]. As discussed in relation to Fig.…”

Section: B Model Assessmentmentioning

confidence: 99%

Bayesian Inference for Stochastic Multipath Radio Channel Models

Hirsch

Bharti

Pedersen

et al. 2023

IEEE Trans. Antennas Propagat.

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Stochastic radio channel models based on underlying point processes of multipath components have been studied intensively since the seminal papers of Turin and Saleh-Valenzuela. Despite of this, inference regarding parameters of these models has remained a major challenge. Current methods typically have a somewhat ad hoc flavor involving a multitude of steps requiring user specification of tuning parameters. In this paper, we propose to instead adopt the principled framework of Bayesian inference to conduct inference for the Saleh-Valenzuela model. The posterior distribution is not analytically tractable and we therefore compute approximations of the posterior using Markov chain Monte Carlo (MCMC) methods specific to point processes. To demonstrate the flexibility of our approach, we additionally propose a new multipath model and apply our inference method to it. The resulting inference methodology is computationally demanding and our successful implementation relies critically on our novel multipath component updates within the MCMC sampler. We demonstrate the usefulness of our approach on simulated and real radio channel data.

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“…The seminal S-V model [2] is widely used as it is easy to simulate from, but is notoriously difficult to calibrate due to its structure. Even though the model can be analyzed using the theory of spatial point processes [57], [58] and moments derived [59], its likelihood function is unavailable. Recent discussions of the physical interpretation of the S-V model, also outlining some difficulties with the model calibration, is given in [60]- [62].…”

Section: A Application To the Saleh-valenzuela Modelmentioning

confidence: 99%

A General Method for Calibrating Stochastic Radio Channel Models With Kernels

Bharti¹,

Briol

Pedersen

2022

IEEE Trans. Antennas Propagat.

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Calibrating stochastic radio channel models to new measurement data is challenging when the likelihood function is intractable. The standard approach to this problem involves sophisticated algorithms for extraction and clustering of multipath components, following which, point estimates of the model parameters can be obtained using specialized estimators. We propose a likelihood-free calibration method using approximate Bayesian computation. The method is based on the maximum mean discrepancy, which is a notion of distance between probability distributions. Our method not only by-passes the need to implement any high-resolution or clustering algorithm, but is also automatic in that it does not require any additional input or manual pre-processing from the user. It also has the advantage of returning an entire posterior distribution on the value of the parameters, rather than a simple point estimate. We evaluate the performance of the proposed method by fitting two different stochastic channel models, namely the Saleh-Valenzuela model and the propagation graph model, to both simulated and measured data. The proposed method is able to estimate the parameters of both the models accurately in simulations, as well as when applied to 60 GHz indoor measurement data.

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Second-Order Spectral Statistics for the Power Gain of Wideband Wireless Channels

Cited by 7 publications

References 21 publications

Degrees of Freedom of Ray-Based Models for mm-Wave Wideband MIMO-OFDM

Degrees of Freedom of Ray-Based Models for mm-Wave Wideband MIMO-OFDM

Bayesian Inference for Stochastic Multipath Radio Channel Models

A General Method for Calibrating Stochastic Radio Channel Models With Kernels

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