Self-Improving Interference Management Based on Deep Learning With Uncertainty Quantification

• URL: http://arxiv.org/abs/2401.13206v1
• Date: Wed, 24 Jan 2024 03:28:48 GMT
• Title: Self-Improving Interference Management Based on Deep Learning With Uncertainty Quantification
• Authors: Hyun-Suk Lee, Do-Yup Kim, Kyungsik Min
• Abstract summary: This paper presents a self-improving interference management framework tailored for wireless communications. Our approach addresses the computational challenges inherent in traditional optimization-based algorithms. A breakthrough of our framework is its acknowledgment of the limitations inherent in data-driven models.
• Score: 10.403513606082067
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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