Hyper-parameter Optimization for Wireless Network Traffic Prediction Models with A Novel Meta-Learning Framework

TL;DR

This paper introduces a meta-learning framework combining attention-based neural networks, KNN, GA, and GRN to optimize hyper-parameters for wireless network traffic prediction models, improving speed and accuracy.

Contribution

It presents a novel meta-learning approach integrating multiple algorithms to rapidly and effectively optimize hyper-parameters for NTP models, outperforming traditional methods.

Findings

Faster hyper-parameter optimization compared to Bayesian, GA, and PSO methods.

Improved prediction accuracy of wireless network traffic models.

Reduced optimization time while maintaining high performance.

Abstract

This paper proposes a novel meta-learning based hyper-parameter optimization framework for wireless network traffic prediction (NTP) models. The primary objective is to accumulate and leverage the acquired hyper-parameter optimization experience, enabling the rapid determination of optimal hyper-parameters for new tasks. In this paper, an attention-based deep neural network (ADNN) is employed as the base-learner to address specific NTP tasks. The meta-learner is an innovative framework that integrates meta-learning with the k-nearest neighbor algorithm (KNN), genetic algorithm (GA), and gated residual network (GRN). Specifically, KNN is utilized to identify a set of candidate hyper-parameter selection strategies for a new task, which then serves as the initial population for GA, while a GRN-based chromosome screening module accelerates the validation of offspring chromosomes, ultimately determining the optimal hyper-parameters. Experimental results demonstrate that, compared to traditional methods such as Bayesian optimization (BO), GA, and particle swarm optimization (PSO), the proposed framework determines optimal hyper-parameters more rapidly, significantly reduces optimization time, and enhances the performance of the base-learner. It achieves an optimal balance between optimization efficiency and prediction accuracy.