Cellular Traffic Prediction via Deep State Space Models with Attention Mechanism

• URL: http://arxiv.org/abs/2506.15688v1
• Date: Mon, 26 May 2025 04:32:15 GMT
• Title: Cellular Traffic Prediction via Deep State Space Models with Attention Mechanism
• Authors: Hui Ma, Kai Yang, Man-On Pun,
• Abstract summary: This paper proposes an end-to-end framework with two variants to explicitly characterize the patterns of cellular traffic among neighboring cells.<n>It uses convolutional neural networks with an attention mechanism to capture spatial dynamics and Kalman filter for temporal degradation.<n>We conduct extensive experiments on three real-world datasets.
• Score: 4.372157417558764
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cellular traffic prediction is of great importance for operators to manage network resources and make decisions. Traffic is highly dynamic and influenced by many exogenous factors, which would lead to the degradation of traffic prediction accuracy. This paper proposes an end-to-end framework with two variants to explicitly characterize the spatiotemporal patterns of cellular traffic among neighboring cells. It uses convolutional neural networks with an attention mechanism to capture the spatial dynamics and Kalman filter for temporal modelling. Besides, we can fully exploit the auxiliary information such as social activities to improve prediction performance. We conduct extensive experiments on three real-world datasets. The results show that our proposed models outperform the state-of-the-art machine learning techniques in terms of prediction accuracy.

Related papers

• Deep learning framework for action prediction reveals multi-timescale locomotor control [41.985053522482545]
  We develop a deep learning-based framework for action prediction.<n>We find that neural network architectures with flexible input history- timescales, like GRU and Transformer, perform best overall.
  arXiv  Detail & Related papers  (2025-03-20T16:57:15Z)
• Deep Learning-driven Mobile Traffic Measurement Collection and Analysis [0.43512163406552007]
  In this thesis, we harness the powerful hierarchical feature learning abilities of Deep Learning (DL) techniques in both spatial and temporal domains. We develop solutions for precise city-scale mobile traffic analysis and forecasting.
  arXiv  Detail & Related papers  (2024-10-14T06:53:45Z)
• Cellular Traffic Prediction Using Online Prediction Algorithms [5.416701003120508]
  This paper investigates the efficacy of live prediction algorithms for forecasting cellular network traffic in real-time scenarios. We apply two live prediction algorithms on machine learning models, one of which is recently proposed Fast LiveStream Prediction (FLSP) algorithm. Our study reveals that the FLSP algorithm can halve the required bandwidth for asynchronous data reporting compared to conventional online prediction algorithms.
  arXiv  Detail & Related papers  (2024-05-08T17:36:14Z)
• Counterfactual Explanations for Deep Learning-Based Traffic Forecasting [42.31238891397725]
  This study aims to leverage an Explainable AI approach, counterfactual explanations, to enhance the explainability and usability of deep learning-based traffic forecasting models. The study first implements a deep learning model to predict traffic speed based on historical traffic data and contextual variables. Counterfactual explanations are then used to illuminate how alterations in these input variables affect predicted outcomes.
  arXiv  Detail & Related papers  (2024-05-01T11:26:31Z)
• Implicit Occupancy Flow Fields for Perception and Prediction in Self-Driving [68.95178518732965]
  A self-driving vehicle (SDV) must be able to perceive its surroundings and predict the future behavior of other traffic participants. Existing works either perform object detection followed by trajectory of the detected objects, or predict dense occupancy and flow grids for the whole scene. This motivates our unified approach to perception and future prediction that implicitly represents occupancy and flow over time with a single neural network.
  arXiv  Detail & Related papers  (2023-08-02T23:39:24Z)
• Dynamic Causal Graph Convolutional Network for Traffic Prediction [19.759695727682935]
  We propose an approach for predicting traffic that embeds time-varying dynamic network to capture finetemporal patterns of traffic data. We then use graph convolutional networks to generate traffic forecasts. Our experimental results on a real traffic dataset demonstrate the superior prediction performance of the proposed method.
  arXiv  Detail & Related papers  (2023-06-12T10:46:31Z)
• Attention-based Spatial-Temporal Graph Neural ODE for Traffic Prediction [3.4806267677524896]
  We propose attention-based graph neural ODE (AST) that explicitly learns the dynamics of the traffic system. Our model aggregates traffic patterns of different periods and has satisfactory performance on two real-world traffic data sets.
  arXiv  Detail & Related papers  (2023-05-01T00:58:48Z)
• Adaptive Hybrid Spatial-Temporal Graph Neural Network for Cellular Traffic Prediction [19.88734776818291]
  We propose a novel deep learning network architecture to tackle the cellular traffic prediction problem. First, we apply adaptive hybrid graph learning to learn the compound spatial correlations among cell towers. Second, we implement a Temporal Convolution Module with multi-periodic temporal data input to capture the nonlinear temporal dependencies.
  arXiv  Detail & Related papers  (2023-02-28T06:46:50Z)
• PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction [78.05103666987655]
  spatial-temporal Graph Neural Network (GNN) models have emerged as one of the most promising methods to solve this problem. We propose a novel propagation delay-aware dynamic long-range transFormer, namely PDFormer, for accurate traffic flow prediction. Our method can not only achieve state-of-the-art performance but also exhibit competitive computational efficiency.
  arXiv  Detail & Related papers  (2023-01-19T08:42:40Z)
• Preference Enhanced Social Influence Modeling for Network-Aware Cascade Prediction [59.221668173521884]
  We propose a novel framework to promote cascade size prediction by enhancing the user preference modeling. Our end-to-end method makes the user activating process of information diffusion more adaptive and accurate.
  arXiv  Detail & Related papers  (2022-04-18T09:25:06Z)
• Spatial-Temporal Tensor Graph Convolutional Network for Traffic Prediction [46.762437988118386]
  We propose a factorized Spatial-Temporal Graph Convolutional Network to deal with traffic speed prediction. To reduce the computational burden, we take Tucker tensor decomposition and derive factorized a tensor convolution. Experiments on two real-world traffic speed datasets demonstrate our method is more effective than those traditional traffic prediction methods.
  arXiv  Detail & Related papers  (2021-03-10T15:28:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.