Estimator for Stochastic Channel Model without Multipath Extraction using Temporal Moments

Bharti, Ayush; Adeogun, Ramoni; Pedersen, Troels

doi:10.1109/spawc.2019.8815389

Cited by 13 publications

(17 citation statements)

References 18 publications

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“…Such an estimator for the Saleh and Valenzuela model [3] was proposed in [15] where the estimation problem was framed as an optimization problem that fitted summary statistics of the data with approximate analytical expressions. More recently, multipath extraction-free calibration methods based on sampling [16] and method of moments [17] have been developed and applied to the Turin model. These methods summarize the data into certain statistics, and rely on explicit derivation of equations linking their means and covariances to the model parameters.…”

Section: Introductionmentioning

confidence: 99%

Learning Parameters of Stochastic Radio Channel Models From Summaries

Bharti

Adeogun

Pedersen

2020

IEEE Open J. Antennas Propag.

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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.

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Section: Introductionmentioning

confidence: 99%

Learning Parameters of Stochastic Radio Channel Models From Summaries

Bharti

Adeogun

Pedersen

2020

IEEE Open J. Antennas Propag.

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“…As the MMD compares infinitely many summaries of the two data-sets, it works better than comparing only the loworder moments such as the means and covariances of the temporal moments as in [19], [20], [22]. When choosing a characteristic kernel, the MMD also guarantees that distributions are uniquely identified by these moments, unlike the case when comparing a finite number of moments.…”

Section: Discussionmentioning

confidence: 99%

“…They have been used to calibrate the Turin model [1], the Saleh-Valenzuela (S-V) model [2] and the polarized propagation graph (PG) model [15]. These calibration methods rely either on a Monte Carlo approximation of the likelihood [16], [17], the method of moments [18], [19], or a summarybased likelihood-free inference framework [20]- [23] such as approximate Bayesian computation (ABC). First developed in the field of population genetics in 1997, ABC has since become a popular method for calibrating models with intractable likelihoods in various fields, see [24] for an overview.…”

Section: Introductionmentioning

confidence: 99%

“…First developed in the field of population genetics in 1997, ABC has since become a popular method for calibrating models with intractable likelihoods in various fields, see [24] for an overview. The main drawback of the calibration methods [17]- [19] is their reliance on equations that explicitly link the moments of the summaries with the model parameters, or in case of [16], on the model-specific point process. These methods should therefore be re-derived for each new model.…”

Section: Introductionmentioning

confidence: 99%

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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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“…From the exponential slope that represents the decay rate of reverberant multipath, the reverberation time [24], denoted as T rev , can be further estimated: where slope has the unit of dB per second. Observation domain with fixed delay or power range can be applied to the PDP to calculate T rev , and typically the range is visually determined by taking into account the effects of noise floor [23], [25]- [28].…”

Section: ) Temporal Domain Channel Propertymentioning

confidence: 99%

Measurement-Based Feasibility Exploration on Detecting and Localizing Multiple Humans Using MIMO Radio Channel Properties

et al. 2020

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This paper explores the feasibility of using the multiple-input multiple-output (MIMO) radio channel properties to passively detect and localize multiple humans in indoor environments. We propose to utilize the unique reverberation characteristics of indoor channels for the purpose of detecting, and the power angular delay profile (PADP) for localizing humans. On the one hand, the reverberation time corresponds with the decay rate of multipath in a closed or partially closed cavity, and varies with the change of the number of humans or the moving of humans relative to the antennas at link ends. On the other hand, the PADP is proposed to be calculated by the Multiple Signal Classification (MUSIC) super resolution algorithm with frequency smoothing preprocessing. The proposed approach is evaluated based on real-world MIMO radio channel measurements obtained from a meeting room. Measurements with and without the presence of humans have been conducted, where the maximum number of humans considered is four. Humans facing different directions, either in parallel or orthogonal to the direct line between the transmit and the receive antennas have been taken into account. In term of the detection feasibility, it is found that the change of the number of humans as well as the change of their facing/moving directions inside the partial reverberant region can be reflected on the change of the reverberation time estimated from the power delay profile of channel. In term of the localization feasibility, it is found that single human location can be well associated to the peak of the variation of the PADP during his/her movement, while multiple humans' movements result in obvious power variation in the very vicinity of some of them, and also in the vicinity of some background objects that is far from target humans. INDEX TERMS Indoor radio channel, MIMO, reverberation time, power delay angular profile, passive detection and localization of humans.

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Estimator for Stochastic Channel Model without Multipath Extraction using Temporal Moments

Cited by 13 publications

References 18 publications

Learning Parameters of Stochastic Radio Channel Models From Summaries

Learning Parameters of Stochastic Radio Channel Models From Summaries

A General Method for Calibrating Stochastic Radio Channel Models With Kernels

Measurement-Based Feasibility Exploration on Detecting and Localizing Multiple Humans Using MIMO Radio Channel Properties

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