Semantic Communication-Oriented Traffic Forecasting with Large Language Model-Augmented Network Intelligence
Keywords:
semantic communication, traffic forecasting, large language models, network intelligence, system architecture, sustainabilityAbstract
The rapid evolution of next-generation communication networks toward semantic-aware paradigms demands fundamentally new approaches to traffic forecasting that go beyond conventional bit-level prediction. This paper investigates the integration of large language models into semantic communication-oriented network intelligence, proposing a system architecture where traffic forecasting is driven by meaning, intent, and contextual understanding rather than raw packet statistics. We formulate the paradigm of semantic traffic forecasting, in which spatiotemporal dynamics are interpreted through the lens of communicated tasks and semantic relevance, enabling resource allocation and congestion control to operate on a goal-oriented basis. The architecture leverages a pre-trained large language model as a semantic engine that fuses multimodal network telemetry, protocol semantics, and environmental context to generate hierarchical forecasts with explanatory reasoning. We analyze structural trade-offs between centralized and distributed deployment, probing the deep interplay between inference latency, energy consumption, and forecast granularity. Detailed attention is given to infrastructure requirements, including data ingestion pipelines for semantic labels, the design of prompt interfaces that encode spatiotemporal network state, and the challenges of incremental model refinement without catastrophic forgetting. Robustness is examined under distributional shifts and adversarial semantic perturbations, while fairness and governance frameworks are developed to address the risk of semantic bias propagation across heterogeneous user groups and service types. The paper further discusses lifecycle sustainability, exploring how foundation model reuse and modular architectures can reduce the carbon footprint of continuous network intelligence. Through a comprehensive interdisciplinary lens, we articulate a research roadmap that aligns semantic communication principles with the emergent capabilities of large language models, ultimately arguing for a re-conceptualization of traffic forecasting as a semantic inference problem embedded within socially aware network infrastructures.
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