Co-Channel Interference Mitigation Using Deep Learning for Drone-Based Large-Scale Antenna Measurements

TL;DR

This paper introduces a lightweight deep learning model that accurately estimates antenna response signals in UAV measurements, effectively mitigating co-channel interference where traditional methods fail, enabling reliable in-field antenna characterization.

Contribution

A novel compact deep convolutional neural network designed to estimate CW tone amplitude under severe interference conditions in UAV-based antenna measurements.

Findings

Achieves 7% MAE across a wide SIR range

Maintains <1 dB error for SIR >= -30 dB

Suitable for embedded UAV deployment

Abstract

Unmanned aerial vehicles (UAVs) enable efficient in-situ radiation characterization of large-aperture antennas directly in their deployment environments. In such measurements, a continuous-wave (CW) probe tone is commonly transmitted to characterize the antenna response. However, active co-channel emissions from neighboring antennas often introduce severe in-band interference, where classical FFT-based estimators fail to accurately estimate the CW tone amplitude when the signal-to-interference ratios (SIR) falls below -10 dB. This paper proposes a lightweight deep convolutional neural network (DC-CNN) that estimates the amplitude of the CW tone. The model is trained and evaluated on real 5~GHz measurement bursts spanning an effective SIR range of --33.3 dB to +46.7 dB. Despite its compact size (<20k parameters), the proposed DC-CNN achieves a mean absolute error (MAE) of 7% over the full range, with <1 dB error for SIR >= -30 dB. This robustness and efficiency make DC-CNN suitable for deployment on embedded UAV platforms for interference-resilient antenna pattern characterization.