PRGA gates wireless intent execution with progressive evidence stages, cutting time-to-first-safe-action by 23-27% and control-plane bytes by 52-54% on 3GPP benchmarks while rejecting all stale inputs and staying within a 0.5pp unsafe-action margin.
Co- mAgent: Multi-LLM based agentic AI empowered intelligent wireless networks
3 Pith papers cite this work. Polarity classification is still indexing.
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The paper presents GENESIS, an agentic AI framework for autonomous 6G RAN synthesis, research, and testing that converts intents into over-the-air validated solutions via composable primitives and a knowledge layer.
Survey classifying 78 joint OFDM-RIS optimization papers into convex relaxation, heuristics, deep learning, and foundation model paradigms, with synthesis showing ML methods achieve near model-based spectral efficiency at much higher speed.
citing papers explorer
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Executor-Side Progressive Risk-Gated Actuation for Agentic AI in Wireless Supervisory Control
PRGA gates wireless intent execution with progressive evidence stages, cutting time-to-first-safe-action by 23-27% and control-plane bytes by 52-54% on 3GPP benchmarks while rejecting all stale inputs and staying within a 0.5pp unsafe-action margin.
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GENESIS: Harnessing AI Agents for Autonomous 6G RAN Synthesis, Research, and Testing
The paper presents GENESIS, an agentic AI framework for autonomous 6G RAN synthesis, research, and testing that converts intents into over-the-air validated solutions via composable primitives and a knowledge layer.
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Optimization Algorithms for Joint OFDM Waveform Design and RIS Configuration in 6G Networks: From Convex Relaxation to Foundation Models
Survey classifying 78 joint OFDM-RIS optimization papers into convex relaxation, heuristics, deep learning, and foundation model paradigms, with synthesis showing ML methods achieve near model-based spectral efficiency at much higher speed.