6G networks need LLM-based agents in a layered semantic control plane to achieve autonomous intelligence, with empirical results showing that heterogeneous deployment across device-edge-core is required due to inherent tradeoffs in reasoning, latency, and efficiency.
Large language models for next-generation wireless network management: A survey and tutorial
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Position paper proposes replacing fragmented narrow AI models with LLMs as the cognitive orchestrator in the RAN Intelligent Controller for Level 5 autonomous 6G networks.
citing papers explorer
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6G Needs Agents: Toward Agentic AI-Native Networks for Autonomous Intelligence
6G networks need LLM-based agents in a layered semantic control plane to achieve autonomous intelligence, with empirical results showing that heterogeneous deployment across device-edge-core is required due to inherent tradeoffs in reasoning, latency, and efficiency.
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Agents Should Replace Narrow Predictive AI as the Orchestrator in 6G AI-RAN
Position paper proposes replacing fragmented narrow AI models with LLMs as the cognitive orchestrator in the RAN Intelligent Controller for Level 5 autonomous 6G networks.