Distilling Knowledge from Resource Management Algorithms to Neural Networks: A Unified Training Assistance Approach

TL;DR

This paper introduces a knowledge distillation approach that uses traditional SINR optimization algorithms as teachers to improve neural network training for resource management, enhancing performance and convergence in wireless systems.

Contribution

It proposes a unified training assistance method combining traditional optimization with neural networks via knowledge distillation, addressing training challenges in wireless resource management.

Findings

Enhanced performance of neural networks in SINR optimization.

Faster convergence in training neural networks.

Improved training efficiency in reinforcement learning.

Abstract

As a fundamental problem, numerous methods are dedicated to the optimization of signal-to-interference-plus-noise ratio (SINR), in a multi-user setting. Although traditional model-based optimization methods achieve strong performance, the high complexity raises the research of neural network (NN) based approaches to trade-off the performance and complexity. To fully leverage the high performance of traditional model-based methods and the low complexity of the NN-based method, a knowledge distillation (KD) based algorithm distillation (AD) method is proposed in this paper to improve the performance and convergence speed of the NN-based method, where traditional SINR optimization methods are employed as ``teachers" to assist the training of NNs, which are ``students", thus enhancing the performance of unsupervised and reinforcement learning techniques. This approach aims to alleviate common issues encountered in each of these training paradigms, including the infeasibility of obtaining optimal solutions as labels and overfitting in supervised learning, ensuring higher convergence performance in unsupervised learning, and improving training efficiency in reinforcement learning. Simulation results demonstrate the enhanced performance of the proposed AD-based methods compared to traditional learning methods. Remarkably, this research paves the way for the integration of traditional optimization insights and emerging NN techniques in wireless communication system optimization.