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Integrating Optimization Theory with Deep Learning for Wireless Network Design

Sinem Coleri, Aysun Gurur Onalan, Marco di Renzo

TL;DR

The paper tackles the complexity of traditional wireless network design and the limitations of pure deep learning by proposing a structured optimization-theory–deep-learning framework. The core idea is to build the block diagram of an optimization-based solution and selectively replace input, algorithm, and output blocks with DNNs, preserving analytical insight while benefiting from data-driven adaptability. This approach reduces online data requirements, communication overhead, and delay, and enhances accuracy and convergence. A Wireless Powered Communication Network case study demonstrates that the XAI+DNN+OSM variant achieves near-optimal performance with strong runtime scalability, underscoring the practical impact and interpretability of the framework.

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.

Integrating Optimization Theory with Deep Learning for Wireless Network Design

TL;DR

The paper tackles the complexity of traditional wireless network design and the limitations of pure deep learning by proposing a structured optimization-theory–deep-learning framework. The core idea is to build the block diagram of an optimization-based solution and selectively replace input, algorithm, and output blocks with DNNs, preserving analytical insight while benefiting from data-driven adaptability. This approach reduces online data requirements, communication overhead, and delay, and enhances accuracy and convergence. A Wireless Powered Communication Network case study demonstrates that the XAI+DNN+OSM variant achieves near-optimal performance with strong runtime scalability, underscoring the practical impact and interpretability of the framework.

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.

Paper Structure

This paper contains 8 sections, 5 figures.

Table of Contents

  1. Introduction
  2. Optimization Theory Based Approach
  3. Deep Learning based Data Driven Approach
  4. Optimization Theory Based Deep Learning Approach
  5. Case Study: Minimum Length Scheduling for Wireless Powered Communication Networks
  6. Implementation of Proposed Algorithms
  7. Performance Evaluation
  8. Conclusion

Figures (5)

  • Figure 1: Optimization theory based approach.
  • Figure 2: Deep learning based data driven approach.
  • Figure 3: Optimization theory based deep learning approach.
  • Figure 4: The block diagrams of OPT, DNN, XAI+DNN, XAI+DNN+OSM and XAI+SB-DNN+OSM, and their relation to each other.
  • Figure 5: Schedule length, runtime and normalized validation loss of the algorithms.