A Noval Satellite Network Traffic Prediction Method Based on GCN-GRU

Li, Yang; Gu, Xiangxiang; Shi, Huaifeng

doi:10.1109/wcsp49889.2020.9299774

Cited by 25 publications

(21 citation statements)

References 4 publications

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“…GNNs are enjoying an increasing popularity in the wireless communication community. In addition to power allocation [9]- [12], GNNs have been used to address cellular [25] and satellite [26] traffic prediction, link scheduling [27], channel control [28], and localization [29]. Due to their localized nature, GNNs have also been applied to cooperative [30] and decentralized [31] control problems in networked systems.…”

Section: Prior Workmentioning

confidence: 99%

“…l are drawn according to (26). The temperature parameter λ > 0 controls the extent to which random variable S(i) τ,l resembles the one-hot representation ( 25): As the temperature tends to zero, the sample S(i) τ,l becomes identical to S 22) allows us to address the inner optimization problems in (22) over the assignment probabilities.…”

Section: B Determining the Module Assignmentmentioning

confidence: 99%

“…The temperature parameter λ > 0 controls the extent to which random variable S(i) τ,l resembles the one-hot representation ( 25): As the temperature tends to zero, the sample S(i) τ,l becomes identical to S 22) allows us to address the inner optimization problems in (22) over the assignment probabilities. To this end, the objective in ( 22) is estimated by drawing samples (i) l from (26) and plugging (27) into the objective function in (22). As a result, we obtain a differentiable function with respect to the parameters η τ,l , which can now be optimized via SGD.…”

Section: B Determining the Module Assignmentmentioning

confidence: 99%

“…of the Gumbel random variables in (26). For a fixed set M, the inner optimization problem in (22) can be written as max…”

Section: B Determining the Module Assignmentmentioning

confidence: 99%

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Modular Meta-Learning for Power Control via Random Edge Graph Neural Networks

Nikoloska¹,

Simeone²

2021

Preprint

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In this paper, we consider the problem of power control for a wireless network with an arbitrarily time-varying topology, including the possible addition or removal of nodes. A data-driven design methodology that leverages graph neural networks (GNNs) is adopted in order to efficiently parametrize the power control policy mapping the channel state information (CSI) to transmit powers. The specific GNN architecture, known as random edge GNN (REGNN), defines a non-linear graph convolutional filter whose spatial weights are tied to the channel coefficients. While prior work assumed a joint training approach whereby the REGNN-based policy is shared across all topologies, this paper targets adaptation of the power control policy based on limited CSI data regarding the current topology. To this end, we propose both black-box and modular meta-learning techniques. Black-box meta-learning optimizes a general-purpose adaptation procedure via (stochastic) gradient descent, while modular meta-learning finds a set of reusable modules that can form components of a solution for any new network topology.Numerical results validate the benefits of meta-learning for power control problems over joint training schemes, and demonstrate the advantages of modular meta-learning when data availability is extremely limited.

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Section: Prior Workmentioning

confidence: 99%

Section: B Determining the Module Assignmentmentioning

confidence: 99%

Section: B Determining the Module Assignmentmentioning

confidence: 99%

“…of the Gumbel random variables in (26). For a fixed set M, the inner optimization problem in (22) can be written as max…”

Section: B Determining the Module Assignmentmentioning

confidence: 99%

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Modular Meta-Learning for Power Control via Random Edge Graph Neural Networks

Nikoloska¹,

Simeone²

2021

Preprint

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“…e model uses GCN to learn network topology and extract spatial characteristics of traffic and uses GRU to learn the temporal characteristics of network traffic. us, the intelligent prediction of network traffic is realized [15]. Although these models have achieved excellent prediction accuracy, most models tend to extract static spatial dependencies in traffic, and such spatial dependencies may evolve over time [16,17].…”

Section: Introductionmentioning

confidence: 99%

AGG: A Novel Intelligent Network Traffic Prediction Method Based on Joint Attention and GCN-GRU

Shi

Pan

et al. 2021

Security and Communication Networks

Self Cite

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Timely and accurate network traffic prediction is a necessary means to realize network intelligent management and control. However, this work is still challenging considering the complex temporal and spatial dependence between network traffic. In terms of spatial dimension, links connect different nodes, and the network traffic flowing through different nodes has a specific correlation. In terms of spatial dimension, not only the network traffic at adjacent time points is correlated, but also the importance of distant time points is not necessarily less than the nearest time point. In this paper, we propose a novel intelligent network traffic prediction method based on joint attention and GCN-GRU (AGG). The AGG model uses GCN to capture the spatial features of traffic, GRU to capture the temporal features of traffic, and attention mechanism to capture the importance of different temporal features, so as to realize the comprehensive consideration of the spatial-temporal correlation of network traffic. The experimental results on an actual dataset show that, compared with other baseline models, the AGG model has the best performance in experimental indicators, such as root mean square error (RMSE), mean absolute error (MAE), accuracy (ACC), determination coefficient ( R 2 ), and explained variance score (EVS), and has the ability of long-term prediction.

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Software defined network‐based multipath state‐aware routing with traffic prediction in satellite network

Luo,

Ning,

Chen

et al. 2023

Trans Emerging Tel Tech

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Uneven traffic demand in satellite networks can lead to link congestion and influence network communication quality. To tackle this problem, we propose a state‐aware routing algorithm based on traffic prediction to disperse network traffic. Reducing the onboard processing requirements, the routing algorithm in this paper introduces a software‐defined network to decouple the control plane and data plane of the satellite network. Also, to better sense the trend of network state, the routing algorithm devises a Spatial‐Temporal Attention Fusion Graph Neural Network (STAFGNN) to predict the future network state by capturing the spatial‐temporal correlation of satellite network traffic. In the control plane, the routing algorithm senses the network state by predicting the future satellite traffic and aggregates the real‐time states of neighboring satellites to identify the load level of the zone. The data plane forwards data based on the control information. Based on the routing algorithm in this paper, multipath routing is designed to reduce the probability of link congestion by dispersing each end‐to‐end traffic. The simulation results demonstrate that the routing algorithm in this paper reduces the number of congestion links and balances the load distribution. The proposed algorithm provides an idea to introduce deep learning to solve the load balancing problem in satellite networks.

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A Noval Satellite Network Traffic Prediction Method Based on GCN-GRU

Cited by 25 publications

References 4 publications

Modular Meta-Learning for Power Control via Random Edge Graph Neural Networks

Modular Meta-Learning for Power Control via Random Edge Graph Neural Networks

AGG: A Novel Intelligent Network Traffic Prediction Method Based on Joint Attention and GCN-GRU

Software defined network‐based multipath state‐aware routing with traffic prediction in satellite network

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