将优化理论与深度学习结合，提升无线网络设计的效率与准确性。
Integrating Optimization Theory with Deep Learning for Wireless Network Design
- 用神经网络替代优化算法中的关键模块，实现自适应改进。
- 仿真显示该方法运行时间更短，准确率和收敛速度显著提升。
- 适合需要实时性与可解释性的无线网络系统设计者。
传统无线网络设计依赖于特定领域数学模型推导出的优化算法，但因复杂度高，难以适用于动态、实时场景。深度学习虽能缓解复杂性与适应性问题，却存在精度不足、延迟高、可解释性差等缺陷。本文提出一种新方法，将优化理论与深度学习相结合：首先构建基于优化理论的解决方案框图，识别对应最优性条件与迭代求解的关键模块；再将部分模块替换为深度神经网络，以增强系统的自适应性与可解释性。大量仿真实验表明，该混合方法在运行时间上优于纯优化方法，且在精度和收敛速度上显著超越纯深度学习模型。
原文摘要 · Abstract (English)
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.
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