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Open Access

ARTICLE

A Space-Air-Ground Integrated Network Traffic Estimation Algorithm Based on Time-Varying Higher-Order Moments

Xiaoxiong Yang1,2, Yi Zhang1, Dingde Jiang1,*, Shuqing He3
1 School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
2 School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore
3 School of Information Science and Engineering, Linyi University, Linyi, China
* Corresponding Author: Dingde Jiang. Email: email
(This article belongs to the Special Issue: Deep Reinforcement Learning for Space-Air-Ground Integrated Edge Computing: Architectures, Algorithms, and Applications)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.083723

Received 09 April 2026; Accepted 12 May 2026; Published online 10 June 2026

Abstract

With the proliferation of network users, traffic engineering has become increasingly important for the management and optimization of networks. As a crucial component of traffic engineering, the traffic matrix can assist network managers in making informed decisions to optimize resource utilization. However, in the current complex and heterogeneous space-ground integrated network, the cost of direct real-time measurement of traffic matrix is high and the delay is high. To address this challenge, we propose a network traffic estimation algorithm based on time-varying higher-order moments and deep learning, which leverages the time-varying higher-order moments property of traffic to improve the understanding of non-stationary traffic. First, we introduce an extended generalized autoregressive conditional heteroskedasticity model (THM-GARCH) that incorporates higher-order moment information to predict traffic volatility. Then, the THM-GARCH model is integrated with a long short-term memory network, and a dynamic feature update mechanism is developed to address the issue. The experimental results indicate that the proposed algorithm achieves the highest qualitative accuracy among all traffic estimation experiments, with a 17.78% reduction in root mean square error and a 14.69% reduction in mean square error.

Keywords

Traffic estimation; higher-order moments; non-stationary traffic; deep learning

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