Statistical Model of Time-varying Backscatter Power of Monostatic RF Sensing Channels in Urban Canyons

Abhishek Adhikari; Dmitry Chizhik; Gil Zussman; Jakub Sapis; Jinfeng Du; John Drogo; Manuel Almendra; Mauricio Rodriguez; Reinaldo A. Valenzuela; Rodolfo Feick

arxiv: 2605.04292 · v1 · submitted 2026-05-05 · 📡 eess.SP

Statistical Model of Time-varying Backscatter Power of Monostatic RF Sensing Channels in Urban Canyons

Dmitry Chizhik , Jakub Sapis , John Drogo , Abhishek Adhikari , Manuel Almendra , Jinfeng Du , Reinaldo A. Valenzuela , Gil Zussman

show 2 more authors

Mauricio Rodriguez Rodolfo Feick

This is my paper

Pith reviewed 2026-05-08 17:24 UTC · model grok-4.3

classification 📡 eess.SP

keywords backscatter powerurban canyonsmonostatic RF sensingstatistical modelRician distributionlognormal distribution140 GHz6G sensing

0 comments

The pith

A distance-to-wall model predicts average monostatic backscatter power in urban canyons with 3.3 dB RMS error or better.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper develops a statistical model for backscatter power in monostatic RF sensing setups inside city streets with moving objects. It shows that average power depends mainly on how close the antenna is to the nearest building wall, matching real measurements from two cities. Power changes as the receive antenna spins follow a lognormal pattern around that average, while power changes over time fit a Rician distribution whose K-factor also varies lognormally and tracks the power deviation. This setup lets system designers run large-scale 6G sensing evaluations without building detailed 3D maps of every street.

Core claim

A concise outdoor deterministic model of average backscattered power dependent on distance to nearest building-wall reproduces observations with 3.3 dB RMS error or better. Distribution of power variation in azimuth around this average is reproduced within 0.5 dB by a random azimuth spectrum with a lognormal distribution. Temporal fluctuations for various antenna aims and locations were found to be well modeled by a Rician distribution, with lognormally distributed K-factor, with 0.47-0.73 correlation coefficient to backscatter power deviation from mean.

What carries the argument

The deterministic mean backscatter power based on distance to the nearest building wall, combined with a lognormal random azimuth spectrum and Rician temporal statistics whose K-factor is also lognormal.

If this is right

The model reproduces observed backscatter statistics in two cities using only distance to the nearest wall.
It enables efficient large-scale system-level simulations for 6G RF sensing performance without full environmental maps.
Azimuth power deviations stay within 0.5 dB of a lognormal random spectrum.
Temporal power fluctuations follow Rician statistics whose K-factor is lognormal and correlates 0.47-0.73 with deviation from the mean.
A narrowband 140 GHz monostatic sounder with omnidirectional transmit and spinning receive antenna suffices to collect the data.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same distance-based mean plus lognormal/Rician forms could be checked against measurements at lower frequencies such as 28 GHz to test frequency scaling.
The correlation between K-factor and power deviation offers a way to jointly sample mean and variance in simulators without independent random draws.
The approach might be adapted to indoor or suburban clutter by recalibrating the distance-to-wall dependence and distribution parameters.
Replacing detailed ray-tracing clutter maps with these statistics could reduce computation time in network-level sensing studies.

Load-bearing premise

That the fitted patterns from measurements in only Manhattan and Valparaiso will hold for urban canyons in general and that distance-to-wall plus these simple random distributions capture the essential behavior without needing extra site-specific details.

What would settle it

New measurements in a third city with different building heights or street widths where the distance-to-wall model's RMS error exceeds 3.3 dB by a large margin.

read the original abstract

We present a measurement-based statistical model for the backscatter power ratio of monostatic RF sensing in urban canyons with moving clutter, suitable for large-scale system level performance evaluation of RF sensing in 6G networks. A narrowband (CW) 140 GHz sounder used a monostatic radar arrangement with an omnidirectional transmit antenna illuminating streets and a spinning horn 2o receive antenna offset vertically (less than 1 m away) collecting backscattered power as a function of azimuth and time below building height in Manhattan and Valparaiso, Chile. A concise outdoor deterministic model of average backscattered power dependent on distance to nearest building-wall reproduces observations with 3.3 dB RMS error or better. Distribution of power variation in azimuth around this average is reproduced within 0.5 dB by a random azimuth spectrum with a lognormal distribution. Temporal fluctuations for various antenna aims and locations were found to be well modeled by a Rician distribution, with lognormally distributed K-factor, with 0.47-0.73 correlation coefficient to backscatter power deviation from mean. The statistical model does not require a detailed environmental description, aiming to reproduce backscatter clutter statistics (as opposed to a deterministic response) faithfully and efficiently, essential for large-scale system-level performance evaluation.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

This paper gives a simple, measurement-backed statistical model for 140 GHz monostatic backscatter in urban streets that fits the data from two cities with low error, but rests on limited sites without cross-checks.

read the letter

The core contribution is a practical model for average backscatter power that depends only on distance to the nearest wall, plus lognormal azimuth variation and Rician time fluctuations whose K-factor is itself lognormal and correlated 0.47-0.73 with the power offset. The measurements come from a monostatic 140 GHz setup with a spinning horn in Manhattan and Valparaiso, and the reported RMS error stays at or below 3.3 dB for the mean and 0.5 dB for the azimuth spread. That combination is new enough in the specific parameters and the correlation values, and it is aimed squarely at large-scale 6G sensing simulations that cannot afford full ray tracing or site maps. The simplicity is the real strength here; it reproduces clutter statistics without needing detailed geometry, which is exactly what system-level work needs. The numbers look credible for the places they measured. The limitation is the narrow base. All the fits come from two cities only, with no reported hold-out across different street widths, building densities, or a third location. If reflectivity, aspect ratio, or clutter type shifts the distributions, the claimed generality for arbitrary urban canyons does not hold. The abstract also gives no error bars on the fitted parameters, no count of independent segments, and no description of data processing or exclusion rules, so the robustness is hard to judge from what is shown. This is useful for researchers running Monte Carlo evaluations of 6G RF sensing in dense cities who want a lightweight clutter model rather than deterministic predictions. It is worth sending to peer review because the measurements add concrete data at a relevant frequency and the model is clean enough to be tested and extended, though any referee would rightly ask for more sites and validation steps.

Referee Report

2 major / 1 minor

Summary. The paper presents a measurement-based statistical model for the time-varying backscatter power ratio of monostatic RF sensing in urban canyons at 140 GHz, derived from narrowband CW sounder data collected in Manhattan and Valparaiso with an omnidirectional transmit antenna and spinning horn receive antenna. It proposes a deterministic mean backscatter power depending only on distance to the nearest building wall (3.3 dB RMS error or better), a lognormal random azimuth spectrum for power variations around the mean (within 0.5 dB), and Rician temporal fluctuations whose K-factor is lognormally distributed and correlated (0.47-0.73) with power deviation from the mean. The model aims to reproduce clutter statistics efficiently for large-scale 6G system-level evaluation without detailed site geometry.

Significance. The empirical measurements in two cities provide concrete support for the reported RMS errors, distribution matches, and correlation ranges, which is a strength for a measurement-driven approach. If the generalization holds, the concise, low-parameter model would be valuable for efficient simulation of urban RF sensing clutter in 6G evaluations, offering a practical alternative to full deterministic ray-tracing. The lack of cross-validation, however, limits the assessed significance for arbitrary urban canyons.

major comments (2)

[Abstract] Abstract: The central claim that the model reproduces observations with 3.3 dB RMS error or better and is suitable for arbitrary urban canyons rests on fits from measurements in only two cities, yet the abstract (and manuscript) provides no error bars on fitted lognormal parameters, no count of distinct street segments or total data volume, and no cross-validation or hold-out testing across canyon widths or cities; this directly weakens support for the universality of the distance-to-wall mean, lognormal azimuth spectrum, and Rician K-factor distributions.
Model description and results: The assertion that the simple distance-to-nearest-wall deterministic mean plus lognormal/Rician forms capture essential behavior without site-specific geometry is load-bearing for the paper's utility claim, but is not tested for robustness against variations in urban morphology (e.g., street aspect ratio or material reflectivity) beyond the two measured cities, risking that the reported error bounds and distribution shapes are site-specific rather than general.

minor comments (1)

The manuscript would benefit from explicitly stating the number of distinct street segments measured, total temporal data duration per location, and any data exclusion criteria to allow readers to assess the statistical reliability of the fitted distributions and correlation coefficients.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on our manuscript. We address the major comments point by point below, indicating planned revisions to improve clarity and transparency regarding the empirical basis and scope of the model.

read point-by-point responses

Referee: [Abstract] Abstract: The central claim that the model reproduces observations with 3.3 dB RMS error or better and is suitable for arbitrary urban canyons rests on fits from measurements in only two cities, yet the abstract (and manuscript) provides no error bars on fitted lognormal parameters, no count of distinct street segments or total data volume, and no cross-validation or hold-out testing across canyon widths or cities; this directly weakens support for the universality of the distance-to-wall mean, lognormal azimuth spectrum, and Rician K-factor distributions.

Authors: We agree that the abstract and main text would benefit from additional quantitative details on the measurement campaign. In the revised manuscript we will add error bars on the fitted lognormal parameters, the number of distinct street segments sampled in each city, and the total data volume in terms of observation time and samples. The model parameters were derived from the combined dataset across both cities without formal cross-validation or hold-out testing. We will explicitly note this in the revision and add a limitations paragraph acknowledging that, while the two cities provide diversity in urban form, independent validation on further datasets would strengthen claims of broader applicability. revision: partial
Referee: [—] Model description and results: The assertion that the simple distance-to-nearest-wall deterministic mean plus lognormal/Rician forms capture essential behavior without site-specific geometry is load-bearing for the paper's utility claim, but is not tested for robustness against variations in urban morphology (e.g., street aspect ratio or material reflectivity) beyond the two measured cities, risking that the reported error bounds and distribution shapes are site-specific rather than general.

Authors: The proposed model is a compact statistical approximation fitted to observed backscatter statistics rather than a deterministic or physics-based predictor intended to hold for every morphology. The measurements do cover a range of canyon widths and facade types in the two cities. We nevertheless accept that explicit sensitivity tests to parameters such as aspect ratio or reflectivity were not performed. In the revision we will expand the discussion to clarify the model's empirical scope, list the morphological variations present in the data, and qualify the utility claim to reflect that it is supported by measurements from two distinct urban environments. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical statistical model fitted directly to measurements

full rationale

The paper constructs its deterministic mean backscatter power model, lognormal azimuth spectrum, and Rician temporal model by fitting functional forms and parameter distributions to CW 140 GHz monostatic measurements collected in Manhattan and Valparaiso. The reported 3.3 dB RMS error, 0.5 dB azimuth reproduction, and 0.47-0.73 K-factor correlations are direct goodness-of-fit metrics on the same dataset, not predictions generated from the fitted parameters in a closed loop. No equations, self-citations, or uniqueness theorems are invoked to derive the model from itself; the work is self-contained as an empirical characterization without load-bearing external references or renaming of known results.

Axiom & Free-Parameter Ledger

3 free parameters · 1 axioms · 0 invented entities

Model rests on empirically fitted distribution parameters from limited measurements and the domain assumption that urban backscatter statistics are adequately captured by distance-to-wall mean plus lognormal/Rician forms; no new physical entities are introduced.

free parameters (3)

Lognormal parameters for azimuth power spectrum
Fitted to reproduce azimuthal variations within 0.5 dB of measured data
Lognormal parameters for Rician K-factor
Fitted to temporal fluctuation statistics
Correlation coefficient between K-factor and power deviation
Observed range 0.47-0.73 from measurements

axioms (1)

domain assumption Urban canyon backscatter statistics can be represented by a distance-dependent deterministic mean plus lognormal and Rician distributions without detailed environmental geometry
Invoked to justify utility for large-scale system-level simulations

pith-pipeline@v0.9.0 · 5572 in / 1570 out tokens · 67519 ms · 2026-05-08T17:24:20.083020+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

20 extracted references · 20 canonical work pages

[1]

Orbiter ku -band integrated radar and communications subsystem

R. Cager, D. LaFlame and L. Parode, "Orbiter ku -band integrated radar and communications subsystem", IEEE Trans . on Communications , v.26(11), Nov. 1978

work page 1978
[2]

A software defined radar system for joint communication and sensing,

C. W. Rossler, E. Ertin and R. L. Moses, "A software defined radar system for joint communication and sensing," 2011 IEEE RadarCon (RADAR), Kansas City, MO, USA, 2011, pp. 1050-1055

work page 2011
[3]

Future Millimeter-Wave Indoor Systems: A Blueprint for Joint Communication and Sensing,

M. Alloulah and H. Huang, "Future Millimeter-Wave Indoor Systems: A Blueprint for Joint Communication and Sensing," in Computer, v. 52(7), July 2019

work page 2019
[4]

Joint Design of Communication and Sensing for Beyond 5G and 6G Systems,

T. Wild, V. Braun and H. Viswanathan, "Joint Design of Communication and Sensing for Beyond 5G and 6G Systems," IEEE Access, v. 9, 2021

work page 2021
[5]

Feasibility Study on Integrated Sensing and Communication (Release 19)

“Feasibility Study on Integrated Sensing and Communication (Release 19)”, 3GPP TR 22.837 V19.1.0 (2023 -09), Technical Report, 3rd Generation Partnership Project; Technical Specification Group TSG SA

work page 2023
[6]

Channel Modeling for Integrated Sensing and Communications in Vehicular Environments: Conceptualization and Challenges,

C. A. Gutiérrez, J. Rodríguez -Piñeiro, K. Guan, D. W. Matolak, M. Walter and D. B. d. Costa, "Channel Modeling for Integrated Sensing and Communications in Vehicular Environments: Conceptualization and Challenges," IEEE Vehicular Technology Magazine, v. 20(2), June 2025

work page 2025
[7]

Integrated Sensing and Communications: Recent Advances and Ten Open Challenges,

S. Lu et al., "Integrated Sensing and Communications: Recent Advances and Ten Open Challenges," IEEE Internet of Things Journal , v.11(11), June 1, 2024

work page 2024
[8]

The Integrated Sensing and Communication Revolution for 6G: Vision, Techniques, and Applications,

N. González-Prelcic et al., "The Integrated Sensing and Communication Revolution for 6G: Vision, Techniques, and Applications," in Proceedings of the IEEE, 2024

work page 2024
[9]

3rd Generation Partnership Project; Technical Specification Group Radio Access Network; Study on channel model for frequencies from 0.5 to 100 GHz (Release 19), 3GPP TR 38.901 v19.2.0 Dec., 2025

work page 2025
[10]

Ray Tracing RF Field Prediction: An Unforgiving Validation

E. M. Vitucci, V. Degli-Esposti, F. Fuschini, J. S. Lu, M. Barbiroli, J. N. Wu, M. Zoli,J. J. Zhu, H. L. Bertoni, "Ray Tracing RF Field Prediction: An Unforgiving Validation", International Journal of Antennas and Propagation, vol. 2015

work page 2015
[11]

Geometrical -Empirical Channel Propagation Model for Human Presence at 60 GHz,

A. Bhardwaj, D. Caudill, C. Gentile, J. Chuang, J. Senic and D. G. Michelson, "Geometrical -Empirical Channel Propagation Model for Human Presence at 60 GHz," IEEE Access, v. 9, 2021

work page 2021
[12]

Accurate Urban Path Loss Models Including Diffuse Scatter

D. Chizhik, J. Du, M. Kohli, A. Adhikari, R. Feick; R. A. Valenzuela, "Accurate Urban Path Loss Models Including Diffuse Scatter" , 17th European Conf. on Antennas and Prop. (EuCAP), Florence, Italy, 2023

work page 2023
[13]

G. T. Ruck, D. E. Barrick, W. D. Stuart, C. K. Krichbaum, Radar Cross Section Handbook. Volumes 1 & 2, Plenum Press, January 1, 1970

work page 1970
[14]

Path loss and directional gain measurements at 28 GHz for non-line-of-sight coverage of indoors with corridors

D. Chizhik, J. Du, R. Feick, M. Rodriguez, G. Castro, and R. A. Valenzuela, “Path loss and directional gain measurements at 28 GHz for non-line-of-sight coverage of indoors with corridors” , IEEE Trans. Antennas Propag., v.68(6), Jun. 2020

work page 2020
[15]

IEEE 802.11 Wireless LANs Channel Models for WLAN Sensing Systems, IEEE 802.11-21/0782r5, August 2021

work page 2021
[16]

Quasi-Deterministic Channel Propagation Model for an Urban Environment at 28 GHz,

N. Varshney, J. Wang, C. Lai, C. Gentile, R. Charbonnier and Y. Corre, "Quasi-Deterministic Channel Propagation Model for an Urban Environment at 28 GHz," IEEE Antennas and Wireless Propagation Letters, vol. 20, no. 7, July 2021

work page 2021
[17]

Ray -tracing simulator for radar signals propagation in radar networks,

D. Gubelli, O. A. Krasnov and O. Yarovyi, "Ray -tracing simulator for radar signals propagation in radar networks," 2013 European Radar Conference, Nuremberg, Germany, 2013

work page 2013
[18]

Backscatter Measurements and Statistical Models for RF Sensing in Indoor Cluttered Environments

D. Chizhik, J. Du, J. Sapis, R. A. Valenzuela, A. Adhikari, G. Zussman, M. A. Almendra, M . Rodriguez, R. Feick, “Backscatter Measurements and Statistical Models for RF Sensing in Indoor Cluttered Environments”, IEEE Trans. On Antennas and Propagation, Aug 2025

work page 2025
[19]

Moment -method estimation of the Ricean K -factor,

L. J. Greenstein, D. G. Michelson and V. Erceg, "Moment -method estimation of the Ricean K -factor," IEEE Communications Lett., v 3(6), June 1999

work page 1999
[20]

Ricean K -Factors in Narrow -Band Fixed Wireless Channels: Theory, Experiments, and Statistical Models,

L. J. Greenstein, S. S. Ghassemzadeh, V. Erceg and D. G. Michelson, "Ricean K -Factors in Narrow -Band Fixed Wireless Channels: Theory, Experiments, and Statistical Models," IEEE Trans. on Vehicular Technology, v. 58(8), Oct. 2009

work page 2009

[1] [1]

Orbiter ku -band integrated radar and communications subsystem

R. Cager, D. LaFlame and L. Parode, "Orbiter ku -band integrated radar and communications subsystem", IEEE Trans . on Communications , v.26(11), Nov. 1978

work page 1978

[2] [2]

A software defined radar system for joint communication and sensing,

C. W. Rossler, E. Ertin and R. L. Moses, "A software defined radar system for joint communication and sensing," 2011 IEEE RadarCon (RADAR), Kansas City, MO, USA, 2011, pp. 1050-1055

work page 2011

[3] [3]

Future Millimeter-Wave Indoor Systems: A Blueprint for Joint Communication and Sensing,

M. Alloulah and H. Huang, "Future Millimeter-Wave Indoor Systems: A Blueprint for Joint Communication and Sensing," in Computer, v. 52(7), July 2019

work page 2019

[4] [4]

Joint Design of Communication and Sensing for Beyond 5G and 6G Systems,

T. Wild, V. Braun and H. Viswanathan, "Joint Design of Communication and Sensing for Beyond 5G and 6G Systems," IEEE Access, v. 9, 2021

work page 2021

[5] [5]

Feasibility Study on Integrated Sensing and Communication (Release 19)

“Feasibility Study on Integrated Sensing and Communication (Release 19)”, 3GPP TR 22.837 V19.1.0 (2023 -09), Technical Report, 3rd Generation Partnership Project; Technical Specification Group TSG SA

work page 2023

[6] [6]

Channel Modeling for Integrated Sensing and Communications in Vehicular Environments: Conceptualization and Challenges,

C. A. Gutiérrez, J. Rodríguez -Piñeiro, K. Guan, D. W. Matolak, M. Walter and D. B. d. Costa, "Channel Modeling for Integrated Sensing and Communications in Vehicular Environments: Conceptualization and Challenges," IEEE Vehicular Technology Magazine, v. 20(2), June 2025

work page 2025

[7] [7]

Integrated Sensing and Communications: Recent Advances and Ten Open Challenges,

S. Lu et al., "Integrated Sensing and Communications: Recent Advances and Ten Open Challenges," IEEE Internet of Things Journal , v.11(11), June 1, 2024

work page 2024

[8] [8]

The Integrated Sensing and Communication Revolution for 6G: Vision, Techniques, and Applications,

N. González-Prelcic et al., "The Integrated Sensing and Communication Revolution for 6G: Vision, Techniques, and Applications," in Proceedings of the IEEE, 2024

work page 2024

[9] [9]

3rd Generation Partnership Project; Technical Specification Group Radio Access Network; Study on channel model for frequencies from 0.5 to 100 GHz (Release 19), 3GPP TR 38.901 v19.2.0 Dec., 2025

work page 2025

[10] [10]

Ray Tracing RF Field Prediction: An Unforgiving Validation

E. M. Vitucci, V. Degli-Esposti, F. Fuschini, J. S. Lu, M. Barbiroli, J. N. Wu, M. Zoli,J. J. Zhu, H. L. Bertoni, "Ray Tracing RF Field Prediction: An Unforgiving Validation", International Journal of Antennas and Propagation, vol. 2015

work page 2015

[11] [11]

Geometrical -Empirical Channel Propagation Model for Human Presence at 60 GHz,

A. Bhardwaj, D. Caudill, C. Gentile, J. Chuang, J. Senic and D. G. Michelson, "Geometrical -Empirical Channel Propagation Model for Human Presence at 60 GHz," IEEE Access, v. 9, 2021

work page 2021

[12] [12]

Accurate Urban Path Loss Models Including Diffuse Scatter

D. Chizhik, J. Du, M. Kohli, A. Adhikari, R. Feick; R. A. Valenzuela, "Accurate Urban Path Loss Models Including Diffuse Scatter" , 17th European Conf. on Antennas and Prop. (EuCAP), Florence, Italy, 2023

work page 2023

[13] [13]

G. T. Ruck, D. E. Barrick, W. D. Stuart, C. K. Krichbaum, Radar Cross Section Handbook. Volumes 1 & 2, Plenum Press, January 1, 1970

work page 1970

[14] [14]

Path loss and directional gain measurements at 28 GHz for non-line-of-sight coverage of indoors with corridors

D. Chizhik, J. Du, R. Feick, M. Rodriguez, G. Castro, and R. A. Valenzuela, “Path loss and directional gain measurements at 28 GHz for non-line-of-sight coverage of indoors with corridors” , IEEE Trans. Antennas Propag., v.68(6), Jun. 2020

work page 2020

[15] [15]

IEEE 802.11 Wireless LANs Channel Models for WLAN Sensing Systems, IEEE 802.11-21/0782r5, August 2021

work page 2021

[16] [16]

Quasi-Deterministic Channel Propagation Model for an Urban Environment at 28 GHz,

N. Varshney, J. Wang, C. Lai, C. Gentile, R. Charbonnier and Y. Corre, "Quasi-Deterministic Channel Propagation Model for an Urban Environment at 28 GHz," IEEE Antennas and Wireless Propagation Letters, vol. 20, no. 7, July 2021

work page 2021

[17] [17]

Ray -tracing simulator for radar signals propagation in radar networks,

D. Gubelli, O. A. Krasnov and O. Yarovyi, "Ray -tracing simulator for radar signals propagation in radar networks," 2013 European Radar Conference, Nuremberg, Germany, 2013

work page 2013

[18] [18]

Backscatter Measurements and Statistical Models for RF Sensing in Indoor Cluttered Environments

D. Chizhik, J. Du, J. Sapis, R. A. Valenzuela, A. Adhikari, G. Zussman, M. A. Almendra, M . Rodriguez, R. Feick, “Backscatter Measurements and Statistical Models for RF Sensing in Indoor Cluttered Environments”, IEEE Trans. On Antennas and Propagation, Aug 2025

work page 2025

[19] [19]

Moment -method estimation of the Ricean K -factor,

L. J. Greenstein, D. G. Michelson and V. Erceg, "Moment -method estimation of the Ricean K -factor," IEEE Communications Lett., v 3(6), June 1999

work page 1999

[20] [20]

Ricean K -Factors in Narrow -Band Fixed Wireless Channels: Theory, Experiments, and Statistical Models,

L. J. Greenstein, S. S. Ghassemzadeh, V. Erceg and D. G. Michelson, "Ricean K -Factors in Narrow -Band Fixed Wireless Channels: Theory, Experiments, and Statistical Models," IEEE Trans. on Vehicular Technology, v. 58(8), Oct. 2009

work page 2009