Multipath-cluster channel models

Gubner, John A.; Bhaskar, Badri Narayan; Hao, Kei

doi:10.1109/icuwb.2012.6340426

“…4. Notice that the region count N X (B t ∩B t ) coincides with the random quantity ( 1 ) defined in (14) in Sec. IV-B and its expectation has already been calculated in (15).…”

Section: A Doubly Wide-sense Stationary Special-case Modelmentioning

confidence: 62%

“…In [13], a point process approach has been employed to derive and analyze a non-stationary geometric-stochastic propagation model applicable within satellite-to-vehicle communications. The impulse response model by Saleh and Valenzuela [5] and more recent variations of it [6] have been analyzed in [14]- [16] with new and detailed insight gained. Campbell's Theorem, a standard tool from the theory of spatial point processes, has proven itself instrumental for deriving both well-known and new results via concise and rigorous arguments, e.g.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Stochastic Radio Channels With Temporal Birth-Death Dynamics: A Marked Spatial Point Process Perspective

Jakobsen

¹

,

Pedersen

²

,

Fleury

³

2014

IEEE Trans. Antennas Propagat.

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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

²

,

Pedersen

³

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.

show abstract

“…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 [56], [57] and moments derived [58], 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 [59]- [61].…”

Section: A Application To the Saleh-valenzuela Modelmentioning

confidence: 99%

A General Method for Calibrating Stochastic Radio Channel Models with Kernels

Bharti¹,

Briol²,

Pedersen³

2020

Preprint

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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.

show abstract

Multipath-cluster channel models

Cited by 5 publications

References 8 publications

Analysis of Stochastic Radio Channels With Temporal Birth-Death Dynamics: A Marked Spatial Point Process Perspective

Analysis of Stochastic Radio Channels With Temporal Birth-Death Dynamics: A Marked Spatial Point Process Perspective

A General Method for Calibrating Stochastic Radio Channel Models With Kernels

A General Method for Calibrating Stochastic Radio Channel Models with Kernels

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