Wavelet-Guided Water-Level Estimation for ISAC

• URL: http://arxiv.org/abs/2511.20936v1
• Date: Wed, 26 Nov 2025 00:01:00 GMT
• Title: Wavelet-Guided Water-Level Estimation for ISAC
• Authors: Ayoob Salari, Kai Wu, Khawaja Fahad Masood, Y. Jay Guo, J. Andrew Zhang,
• Abstract summary: Real-time water-level monitoring is vital for flood response, infrastructure management, and environmental forecasting.<n>We propose a passive, low-cost water-level tracking scheme that uses only LTE downlink power metrics reported by commodity receivers.
• Score: 28.187510402999376
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Real-time water-level monitoring across many locations is vital for flood response, infrastructure management, and environmental forecasting. Yet many sensing methods rely on fixed instruments - acoustic, radar, camera, or pressure probes - that are costly to install and maintain and are vulnerable during extreme events. We propose a passive, low-cost water-level tracking scheme that uses only LTE downlink power metrics reported by commodity receivers. The method extracts per-antenna RSRP, RSSI, and RSRQ, applies a continuous wavelet transform (CWT) to the RSRP to isolate the semidiurnal tide component, and forms a summed-coefficient signature that simultaneously marks high/low tide (tide-turn times) and tracks the tide-rate (flow speed) over time. These wavelet features guide a lightweight neural network that learns water-level changes over time from a short training segment. Beyond a single serving base station, we also show a multi-base-station cooperative mode: independent CWTs are computed per carrier and fused by a robust median to produce one tide-band feature that improves stability and resilience to local disturbances. Experiments over a 420 m river path under line-of-sight conditions achieve root-mean-square and mean-absolute errors of 0.8 cm and 0.5 cm, respectively. Under a non-line-of-sight setting with vegetation and vessel traffic, the same model transfers successfully after brief fine-tuning, reaching 1.7 cm RMSE and 0.8 cm MAE. Unlike CSI-based methods, the approach needs no array calibration and runs on standard hardware, making wide deployment practical. When signals from multiple base stations are available, fusion further improves robustness.

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