Reinforcement Learning-Based Resource Allocation for Adaptive Transmission and Retransmission Scheme for URLLC in 5G

Ultra-reliable low-latency communication (URLLC) is one of the service categories envisioned by fifth-generation (5G) wireless systems for supporting mission-critical applications. These applications require the transmission of short-length data packets with a high level of reliability and hard latency bound. Achieving targeted reliability in URLLC is challenging because of dynamic channel conditions and network load. Therefore, retransmission of lost data packets is essential for increasing the reliability of data. Again, an optimized radio resource allocation for these (re)transmission schemes is required to satisfy URLLC constraints. Hence, we propose a reinforcement learning-based resource allocation for adaptive transmission and retransmission scheme. We demonstrate the use of machine learning in optimizing the resource usage under variable network load conditions.

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