# Autoencoder-based Radio Frequency Interference Mitigation For SMAP Passive Radiometer

arXiv: https://arxiv.org/abs/2304.13158 · HTML: https://mlsanity.com/paper/2304.13158/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 11 references that are in the map.
- First posted on arXiv 25 Apr 2023.

## Built on by (most cited first, 2 of 2)

- [Watermarking and Anomaly Detection in Machine Learning Models for LORA RF Fingerprinting](https://mlsanity.com/paper/2509.15170/) (2025)
- [Online Meta-Learning Channel Autoencoder for Dynamic End-to-end Physical Layer Optimization](https://mlsanity.com/paper/2501.01608/) (2025)

## Abstract (arXiv)

Passive space-borne radiometers operating in the 1400-1427 MHz protected frequency band face radio frequency interference (RFI) from terrestrial sources. With the growth of wireless devices and the appearance of new technologies, the possibility of sharing this spectrum with other technologies would introduce more RFI to these radiometers. This band could be an ideal mid-band frequency for 5G and Beyond, as it offers high capacity and good coverage. Current RFI detection and mitigation techniques at SMAP (Soil Moisture Active Passive) depend on correctly detecting and discarding or filtering the contaminated data leading to the loss of valuable information, especially in severe RFI cases. In this paper, we propose an autoencoder-based RFI mitigation method to remove the dominant RFI caused by potential coexistent terrestrial users (i.e., 5G base station) from the received contaminated signal at the passive receiver side, potentially preserving valuable information and preventing the contaminated data from being discarded.