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Mission-critical spectrum sharing with decentralized Multi-Agent Reinforcement Learning
Motivated by emerging mission-critical applications and an increasingly congested spectrum, we develop a decentralized multi-agent reinforcement learning (MARL) model for dynamic spectrum access. The model enables secondary users to learn effective transmission strategies across shared frequency ban
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