Multi-Agent LLM Framework for Dynamic Spectrum Sharing and Traffic Intelligence in Next-Generation Cellular Networks
Keywords:
Multi-agent systems; large language models; dynamic spectrum sharing; traffic intelligence; 6G cellular networks; cognitive radio; network optimization; spectrum governanceAbstract
The convergence of artificial intelligence and next-generation cellular architectures demands novel system frameworks capable of orchestrating highly dynamic and contested resources while preserving fairness, robustness, and sustainability. This paper introduces a multi-agent large language model framework designed for integrated dynamic spectrum sharing and traffic intelligence in sixth-generation and beyond networks. Departing from narrow optimization approaches, the framework positions foundation-model reasoning at the coordination core, leveraging semantically rich world knowledge, contextual planning, and natural-language interfacing to negotiate spectrum access and forecast traffic patterns across heterogeneous network slices. The architectural discussion unfolds along six axes: system-level design principles that fuse large language model agents with cognitive radio functions; dynamic spectrum sharing mechanisms grounded in conversational negotiation and policy-aware reasoning; the fusion of traffic intelligence with spatial-temporal prediction through multi-modal fusion of network telemetry and external signals; structural trade-offs in governance, including explainability, fairness auditing, and conflict resolution among agents; deployment pathways that address energy footprints, model staleness, and edge-cloud continuum partitioning; and policy implications for spectrum regulators and infrastructure operators. Throughout, the analysis emphasizes how large language model agents can serve as deliberative, context-sensitive intermediaries rather than black-box optimizers, enabling transparent, adaptive, and socially aligned spectrum management. The discussion further contextualizes the proposal within ongoing standardization, regulatory debates on shared spectrum access, and the imperative of sustainable artificial intelligence infrastructures.
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