COMLLM uses multi-turn LLM reasoning via GRPO and LACS to achieve near-optimal latency, better fairness, and zero-shot generalization to larger unseen network topologies in mobile edge computing task offloading.
Mobile edge computing: A survey on archi- tecture and computation offloading
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Lyapunov-based lightweight AI agent achieves O(N) complexity for joint PQC-NOMA allocation in edge systems, with claimed 46x speedup over SCA and improved throughput in simulations.
citing papers explorer
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Multi-Turn Reasoning LLMs for Task Offloading in Mobile Edge Computing
COMLLM uses multi-turn LLM reasoning via GRPO and LACS to achieve near-optimal latency, better fairness, and zero-shot generalization to larger unseen network topologies in mobile edge computing task offloading.
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Lightweight Quantum Agent for Edge Systems: Joint PQC and NOMA Resource Allocation
Lyapunov-based lightweight AI agent achieves O(N) complexity for joint PQC-NOMA allocation in edge systems, with claimed 46x speedup over SCA and improved throughput in simulations.