3 papers
eess.SP2025
Interference Mitigation using U-Net Autoencoder based system
Hiten Prakash Kothari, R. Michael Buehrer
This paper proposes a U-Net-based autoencoder framework for mitigating interference in communication signals corrupted by noise and diverse interference sources. The approach targe…
eess.SP2025
On the Ability of Deep Learning to Detect Signals with Unknown Parameters
Tom Anders, Hiten Prakash Kothari, R. Michael Buehrer
In many signal processing applications, including communications, sonar, radar, and localization, a fundamental problem is the detection of a signal of interest in background noise…
eess.SP2025
Interference Mitigation Recommender System using U-Net Autoencoders
Hiten Prakash Kothari, R. Michael Buehrer
Building on the previous work on interference mitigation, this paper introduces a modular recommender system that automatically selects the most effective interference mitigation s…