BERTO introduces a prompt-conditioned BERT framework for cellular traffic forecasting that uses a balancing loss to enable flexible trade-offs between power consumption and SLA violations using natural language inputs.
Spatial- temporal cellular traffic prediction for 5g and beyond: A graph neural networks-based approach
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CoMemNet is a dual-branch continual learning model for dynamic traffic networks that combines contrastive sampling via Wasserstein features and memory replay to achieve SOTA performance while mitigating forgetting.
citing papers explorer
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BERTO: Intent-Driven Network Time Series Forecasting via Natural Language Operator Preferences
BERTO introduces a prompt-conditioned BERT framework for cellular traffic forecasting that uses a balancing loss to enable flexible trade-offs between power consumption and SLA violations using natural language inputs.
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CoMemNet: Contrastive Sampling with Memory Replay Network for Continual Traffic Prediction
CoMemNet is a dual-branch continual learning model for dynamic traffic networks that combines contrastive sampling via Wasserstein features and memory replay to achieve SOTA performance while mitigating forgetting.