An Iterative Transfer Matrix Computation Method for Propagation Graphs in Multiroom Environments

IEEE Open J. Antennas Propag.

2020

Self Cite

Estimating parameters of stochastic radio channel models based on new measurement data is an arduous task usually involving multiple steps such as multipath extraction and clustering. We propose two different machine learning methods, one based on approximate Bayesian computation (ABC) and the other on deep learning, for fitting stochastic channel models to data directly. The proposed methods make use of easy-to-compute summary statistics of measured data instead of relying on extracted multipath components. Moreover, the need for post-processing of the extracted multipath components is omitted. Taking the polarimetric propagation graph model as an example stochastic model, we present relevant summaries and evaluate the performance of the proposed methods on simulated and measured data. We find that the methods are able to learn the parameters of the model accurately in simulations. Applying the methods on 60 GHz indoor measurement data yields parameter estimates that generate averaged power delay profile from the model that fits the data.

Section: Calibration Of Polarimetric Propagation Graph Modelmentioning

confidence: 99%

Learning Parameters of Stochastic Radio Channel Models From Summaries

Bharti

Adeogun

IEEE Open J. Antennas Propag.

2020

Self Cite

“…Several other studies have recently presented applications and/or modifications of the graph based models to various propagation environments such as indoor [13], [14], multiroom [15], [16], indoor-to-outdoor [17], high speed railway [18]- [21] and millimeter wave systems [22]. Hybrid models combining the propagation graph based model with ray tracing approaches have also been studied in [23]- [26].…”

Section: Introductionmentioning

confidence: 99%

Polarimetric Wireless Indoor Channel Modeling Based on Propagation Graph

Adeogun

IEEE Trans. Antennas Propagat.

Gustafson³

et al. 2019

Self Cite

This paper generalizes a propagation graph model to polarized indoor wireless channels. In the original contribution, the channel is modelled as a propagation graph in which vertices represent transmitters, receivers and scatterers while edges represents the propagation conditions between vertices. Each edge is characterized by an edge transfer function accounting for the attenuation, delay spread and phase shift on the edge. In this contribution, we extend this modelling formalism to polarized channels by incorporating depolarization effects into the edge transfer functions and hence, the channel transfer matrix. We derive closed form expressions for the polarimetric power delay spectrum and cross-polarization ratio of the indoor channel. The expressions are derived considering average signal propagation in a graph and relate these statistics to model parameters, thereby providing a useful approach to investigate the averaged effect of these parameters on the channel statistics. Furthermore, we present a procedure for calibrating the model based on method of moments. Simulations were performed to validate the proposed model and the derived approximate expressions using both synthetic data and channel measurements at 15 GHz and 60 GHz. We observe that the model and approximate expressions provide good fits to the measurement data.

“…As our second example, we demonstrate the performance of our proposed method on the PG model. The PG model was first introduced in [63], and since then has been applied to a wide range of scenarios in [64]- [67]. Recently, it has been extended to account for polarization in [15], [68], [69].…”

Section: B Application To the Propagation Graph Modelmentioning

confidence: 99%

A General Method for Calibrating Stochastic Radio Channel Models With Kernels

Bharti¹,

Briol

IEEE Trans. Antennas Propagat.

2022

Self Cite

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.