In this work, we propose a Graph Convolutional Network (GCN)-based architecture for the channel utilization prediction. Since CNN-based models are only suitable ...
Yu et al. proposed the model Spatial-. Temporal Graph Convolutional Networks (STGCN) for traffic speed prediction, which built the graph using the sensor.
Zhu et al. [45] developed a graph convolutional network (GCN) model to predict cellular network channel utilization. CNN-based models are only effective for ...
In this paper, a spatial-temporal parallel prediction model based on graph convolution combined with long and short-term memory networks (STP-GLN) is proposed
Abstract—Cellular traffic prediction enables operators to adapt to traffic demand in real-time for improving network resource utilization and user ...
Cellular traffic prediction enables operators to adapt to traffic demand in real-time for improving network resource utilization and user experience.
This method fully considers cross-information and constructs an attention cross-view between every pair of dimensions among the channel, time and space domains ...
Mar 7, 2024 · The deep graph convolution network outperformed existing methods, offering efficiency and reduced resource consumption in cellular networks.
... By modeling the network as a graph, GCN can effectively process and learn from the relational information inherent in network structures, offering insights ...
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Accurate and timely cellular traffic prediction is essential for resource allocation, base station energy conservation, and network optimization. Recent years ...