Towards Ultra-Reliable 6G in-X Subnetworks: Dynamic Link Adaptation by Deep Reinforcement Learning
The research proposes a deep reinforcement learning method using Soft Actor-Critic (SAC) to dynamically adjust transmission power and blocklength based on SINR in 6G in-X subnetworks. This approach aims to optimize two conflicting goals: minimizing consecutive packet outages for ultra-reliable communication and maximizing energy efficiency. The method outperforms other DRL algorithms by finding the best trade-off between reliability and resource consumption.