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.

本论文提出了一种突破性的自我提升的无线通信干扰管理框架，结合深度学习和不确定性量化，以提高整体系统性能。通过利用深度学习模型来预测最佳的干扰管理解决方案，该方法解决了传统基于优化算法的计算挑战。该框架突破性地认识到数据驱动模型的局限性，特别是在训练数据集未能充分代表的情况下。为了克服这些挑战，我们提出了一种不确定性量化方法，并附带了一个资格标准，以评估模型预测的可信度。该框架在模型生成的解决方案和传统算法之间以策略性的方式交替应用，根据量化的不确定性评估预测的可信度。实验结果验证了该框架的有效性，并展示了其在训练数据集未能充分代表的情况下相对于传统深度学习模型的优越性。本研究是在不确定性量化的视角下，利用自我提升的深度学习进行干扰管理的开创性探索。