[Paper Review] Mobile Radio Networks and Weather Radars Dualism: Rainfall Measurement Revolution in Densely Populated Areas

Davide Bellini, Mario Montopoli+6 more|arXiv (Cornell University)|Mar 19, 2026

Precipitation Measurement and Analysis0 citations

TL;DR

This paper shows how a network of cellular base stations can be repurposed as a dense, radar-like sensor (BS-WRM) to perform high-resolution rainfall measurements in urban areas, including retrieval of reflectivity, mean Doppler velocity, and spectral width.

ABSTRACT

This study demonstrates, for the first time, how a network of cellular base stations (BSs) - the infrastructure of mobile radio networks - can be used as a distributed opportunistic radar for rainfall remote sensing. By adapting signal-processing techniques traditionally employed in Doppler weather radar systems, we demonstrate that BS signals can be used to retrieve typical weather radar products, including reflectivity factor, mean Doppler velocity, and spectral width. Due to the high spatial density of BS infrastructure in urban environments, combined with intrinsic technical features such as electronically steerable antenna arrays and wide receiver bandwidths, the proposed approach achieves unprecedented spatial and temporal resolutions, on the order of a few meters and several tens of seconds, respectively. Despite limitations related to low transmitted power, limited antenna gain, and other system constraints, a major challenge arises from ground clutter contamination, which is exacerbated by the nearly horizontal orientation of BS antenna beams. This work provides a thorough assessment of clutter impact and demonstrates that, through appropriate processing, the resulting clutter-filtered radar moments reach a satisfactory level of quality when compared with raw observations and with measurements from independent BSs with overlapped field-of-views. The findings highlight a transformative opportunity for urban hydrometeorology: leveraging existing telecommunications infrastructure to obtain rainfall information with a level of spatial granularity and temporal immediacy like never before.

Motivation & Objective

• Motivate and demonstrate the feasibility of using cellular base stations as opportunistic weather radars (BS-WRM) for urban rainfall sensing.
• Characterize the potential gains in spatial and temporal resolution offered by dense BS deployments.
• Assess ground clutter impact and develop processing that yields radar moments comparable to traditional measurements.

Proposed method

• Adapt Doppler weather radar signal processing to BS-WRM data streams.
• Compute range-compressed signals via cross-correlation between transmitted and received BS signals (Eq. 4 reference context).
• Estimate Doppler spectrum per range bin using windowing and periodogram, producing S_g and P_rx,g (Eq. 2 and Eq. 3 reference context).
• Relate received power to weather radar quantities through the radar equation framework, including reflectivity Z_g and path losses (Eq. 4 reference context).
• Leverage multi-beam BS architectures to achieve range-resolved precipitation inference in a monostatic-like configuration.
• Address ground clutter and show clutter-filtered radar moments reach satisfactory quality compared with raw observations and overlapped-view measurements.

Experimental results

Research questions

• RQ1 Can commercial 5G/advanced BSs be operated in a weather radar-like mode (WRM) without disrupting normal communications?
• RQ2 What is the achievable range resolution, Doppler resolution, and SNR trade-off when using BS-WRM in urban settings?
• RQ3 How does ground clutter affect rainfall retrieval from BS-WRM, and can processing mitigate it to acceptable levels?
• RQ4 How do BS-WRM measurements compare to traditional weather radar observations and to overlapping BS measurements in terms of quality and timeliness?

Key findings

• BS-WRM can retrieve typical weather radar products (reflectivity, mean Doppler velocity, spectral width) from BS signals.
• The proposed BS-WRM approach yields unprecedented spatial (meters) and temporal (tens of seconds) resolutions in urban environments.
• Ground clutter contamination is a major challenge, but with appropriate processing, clutter-filtered radar moments are of satisfactory quality.
• BS-WRM demonstrates feasibility for tracking extreme precipitation and provides a scalable, urban-centric rainfall sensing solution leveraging existing telecom infrastructure.
• Compared to conventional weather radars, BS-WRM benefits from higher spatial density and potential for near-field, range-resolved precipitation mapping in cities.