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Spatial PDE-aware Selective State-space with Nested Memory for Mobile Traffic Grid Forecasting

arXiv:2603.12353v1 Announce Type: new Abstract: Traffic forecasting in cellular networks is a challenging spatiotemporal prediction problem due to strong temporal dependencies, spatial heterogeneity across cells, and the need for scalability to large network deployments. Traditional cell-specific models incur prohibitive training and maintenance costs, while global models often fail to capture heterogeneous spatial dynamics. Recent spatiotemporal architectures based on attention or graph neural networks improve accuracy but introduce high computational overhead, limiting their applicability in large-scale or real-time settings. We study spatiotemporal grid forecasting, where each time step is a 2D lattice of traffic values, and predict the next grid patch using previous patches. We propose NeST-S6, a convolutional selective state-space model (SSM) with a spatial PDE-aware core, implemented in a nested learning paradigm: convolutional local spatial mixing feeds a spatial PDE-aware SSM

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Zineddine Bettouche, Khalid Ali, Andreas Fischer, Andreas Kassler · · 1 min read · 3 views
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arXiv:2603.12353v1 Announce Type: new Abstract: Traffic forecasting in cellular networks is a challenging spatiotemporal prediction problem due to strong temporal dependencies, spatial heterogeneity across cells, and the need for scalability to large network deployments. Traditional cell-specific models incur prohibitive training and maintenance costs, while global models often fail to capture heterogeneous spatial dynamics. Recent spatiotemporal architectures based on attention or graph neural networks improve accuracy but introduce high computational overhead, limiting their applicability in large-scale or real-time settings. We study spatiotemporal grid forecasting, where each time step is a 2D lattice of traffic values, and predict the next grid patch using previous patches. We propose NeST-S6, a convolutional selective state-space model (SSM) with a spatial PDE-aware core, implemented in a nested learning paradigm: convolutional local spatial mixing feeds a spatial PDE-aware SSM core, while a nested-learning long-term memory is updated by a learned optimizer when one-step prediction errors indicate unmodeled dynamics. On the mobile-traffic grid (Milan dataset) at three resolutions (202, 502, 1002), NeST-S6 attains lower errors than a strong Mamba-family baseline in both single-step and 6-step autoregressive rollouts. Under drift stress tests, our model's nested memory lowers MAE by 48-65% over a no-memory ablation. NeST-S6 also speeds full-grid reconstruction by 32 times and reduces MACs by 4.3 times compared to competitive per-pixel scanning models, while achieving 61% lower per-pixel RMSE.

Executive Summary

This article presents NeST-S6, a novel spatiotemporal grid forecasting model designed to tackle the challenges of mobile traffic forecasting in cellular networks. The proposed model combines a convolutional selective state-space model with a spatial PDE-aware core, implemented in a nested learning paradigm. Experimental results demonstrate the efficacy of NeST-S6 in achieving lower errors than a strong baseline model across various resolutions and autoregressive rollouts. Moreover, the nested memory component of NeST-S6 significantly outperforms a no-memory ablation in drift stress tests. The model also exhibits improved computational efficiency compared to competitive per-pixel scanning models. Overall, the study makes a valuable contribution to the field of spatiotemporal grid forecasting, offering a scalable and accurate solution for mobile traffic forecasting in large-scale network deployments.

Key Points

  • ▸ NeST-S6 combines a convolutional selective state-space model with a spatial PDE-aware core.
  • ▸ The nested learning paradigm enables efficient learning and adaptation to unmodeled dynamics.
  • ▸ Experimental results demonstrate the efficacy of NeST-S6 in achieving lower errors and improved computational efficiency.

Merits

Scalability and Accuracy

NeST-S6 offers a scalable and accurate solution for mobile traffic forecasting in large-scale network deployments, making it a valuable contribution to the field of spatiotemporal grid forecasting.

Efficient Learning Paradigm

The nested learning paradigm enables efficient learning and adaptation to unmodeled dynamics, reducing the computational overhead associated with traditional spatiotemporal architectures.

Demerits

Complexity and Training Costs

The proposed model may incur higher complexity and training costs compared to traditional cell-specific models or global models, which could be a limitation for certain applications.

Limited Generalizability

The experimental results are primarily based on the Milan dataset, and it remains to be seen whether NeST-S6 generalizes well to other datasets or scenarios.

Expert Commentary

While the proposed model demonstrates excellent performance in mobile traffic forecasting, it is essential to consider the potential trade-offs between accuracy, scalability, and complexity. The nested learning paradigm offers a promising solution for efficient learning and adaptation to unmodeled dynamics, but its applicability may be limited to certain scenarios or datasets. Furthermore, the experimental results are primarily based on the Milan dataset, and it remains to be seen whether NeST-S6 generalizes well to other datasets or scenarios. Nonetheless, the study makes a valuable contribution to the field of spatiotemporal grid forecasting, offering a scalable and accurate solution for mobile traffic forecasting in large-scale network deployments.

Recommendations

  • ✓ Future research should investigate the generalizability of NeST-S6 across different datasets and scenarios.
  • ✓ The trade-offs between accuracy, scalability, and complexity should be thoroughly explored to identify the optimal configuration for specific applications.

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