Multimodal Spatiotemporal-Frequency Fusion with Peak Enhancement for Cellular Traffic Forecasting
arXiv:2607.07016v1 Announce Type: new Abstract: Accurate forecasting of cellular network traffic is essential for network planning, resource allocation, and quality-of-service assurance in modern mobile communication systems. Real-world traffic often exhibits bursty endogenous dynamics and disturbances triggered by external urban events, which makes reliable prediction highly challenging. Most existing spatiotemporal traffic forecasting methods primarily focus on intrinsic traffic patterns or structural relationships within a single modality, and rarely model burst behavior together with exoge
The increasing complexity and demands on cellular networks, driven by advanced applications and urban density, necessitate more accurate traffic forecasting to optimize resource management. New modalities and AI techniques are maturing to address these challenges more effectively.
Improved cellular traffic forecasting directly impacts network efficiency, quality of service, and the ability to proactively manage network resources, which is critical for supporting a connected AI-driven world. It enables better infrastructure planning and reduces operational costs.
The ability to accurately predict bursty and event-driven cellular traffic patterns through multimodal fusion changes how network operators can allocate resources and prevent bottlenecks. This moves beyond traditional methods that primarily focused on intrinsic patterns.
- · Telecommunication companies
- · Smart city developers
- · AI/ML model developers
- · Cloud resource providers
- · Legacy network planning systems
- · Companies relying on reactive network management
More stable and efficient cellular networks capable of higher throughput and lower latency.
Enhanced performance for compute-intensive edge applications, benefiting from predictable network conditions.
Reduced infrastructure investment costs due to optimized resource utilization and proactive capacity adjustments, potentially accelerating AI adoption in mobile contexts.
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Read at arXiv cs.LG