Integrating Optimization Theory with Deep Learning for Wireless Network Design

TL;DR

This paper presents a hybrid approach combining optimization theory and deep learning to improve wireless network design, achieving faster, more accurate, and more interpretable solutions than traditional methods or pure deep learning models.

Contribution

It introduces a novel integration framework that replaces key optimization components with neural networks, enhancing adaptability and interpretability in wireless network design.

Findings

Reduces runtime compared to traditional optimization methods

Improves accuracy and convergence rates over pure deep learning models

Outperforms existing approaches in simulation tests

Abstract

Traditional wireless network design relies on optimization algorithms derived from domain-specific mathematical models, which are often inefficient and unsuitable for dynamic, real-time applications due to high complexity. Deep learning has emerged as a promising alternative to overcome complexity and adaptability concerns, but it faces challenges such as accuracy issues, delays, and limited interpretability due to its inherent black-box nature. This paper introduces a novel approach that integrates optimization theory with deep learning methodologies to address these issues. The methodology starts by constructing the block diagram of the optimization theory-based solution, identifying key building blocks corresponding to optimality conditions and iterative solutions. Selected building blocks are then replaced with deep neural networks, enhancing the adaptability and interpretability of the system. Extensive simulations show that this hybrid approach not only reduces runtime compared to optimization theory based approaches but also significantly improves accuracy and convergence rates, outperforming pure deep learning models.