Guardado en:
| Autores principales: | , , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2605.04292 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866913093537234944 |
|---|---|
| author | Chizhik, Dmitry Sapis, Jakub Drogo, John Adhikari, Abhishek Almendra, Manuel Du, Jinfeng Valenzuela, Reinaldo A. Zussman, Gil Rodriguez, Mauricio Feick, Rodolfo |
| author_facet | Chizhik, Dmitry Sapis, Jakub Drogo, John Adhikari, Abhishek Almendra, Manuel Du, Jinfeng Valenzuela, Reinaldo A. Zussman, Gil Rodriguez, Mauricio Feick, Rodolfo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_04292 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Statistical Model of Time-varying Backscatter Power of Monostatic RF Sensing Channels in Urban Canyons Chizhik, Dmitry Sapis, Jakub Drogo, John Adhikari, Abhishek Almendra, Manuel Du, Jinfeng Valenzuela, Reinaldo A. Zussman, Gil Rodriguez, Mauricio Feick, Rodolfo Signal Processing 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. |
| title | Statistical Model of Time-varying Backscatter Power of Monostatic RF Sensing Channels in Urban Canyons |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2605.04292 |