Self-Improving Interference Management Based on Deep Learning With
  Uncertainty Quantification
Hyun-Suk Lee Do-Yup Kim Kyungsik Min
This paper presents a groundbreaking self-improving interference management
framework tailored for wireless communications, integrating deep learning with
uncertainty quantification to enhance overall system performance. Our approach
addresses the computational challenges inherent in traditional
optimization-based algorithms by harnessing deep learning models to predict
optimal interference management solutions. A significant breakthrough of our
framework is its acknowledgment of the limitations inherent in data-driven
models, particularly in scenarios not adequately represented by the training
dataset. To overcome these challenges, we propose a method for uncertainty
quantification, accompanied by a qualifying criterion, to assess the
trustworthiness of model predictions. This framework strategically alternates
between model-generated solutions and traditional algorithms, guided by a
criterion that assesses the prediction credibility based on quantified
uncertainties. Experimental results validate the framework's efficacy,
demonstrating its superiority over traditional deep learning models, notably in
scenarios underrepresented in the training dataset. This work marks a
pioneering endeavor in harnessing self-improving deep learning for interference
management, through the lens of uncertainty quantification.