# An Adaptive Hybrid Automatic Repeat Request (A-HARQ) Scheme Based on Reinforcement Learning
## 2 Related Works
It takes advantage of the LDPC subcode structure to provide faster feedback, achieving earlier retransmission and exhibiting better false positive energy. This results in fewer transmission failures compared with when E-HARQ predicts NACK as ACK. However, choosing the appropriate threshold is a critical issue for its performance. The authors in [9] improved the SC EHARQ scheme, which was the first exploratory study on the method for predicting quantitative improvement. It uses machine-learning methods to predict the decodability of received messages, using more advanced classification methods to predict decoding results before the final decoder iteration. More complex input features are utilized to further improve the classification performance, and appropriate methods for distinguishing between different classifiers are discussed. In [10], the authors conducted a mathematical analysis of packet errors, retransmissions, and delays related to bandwidth. They applied the M/G/1 queuing model to the delay analysis. The minimum bandwidth required was determined, as well as the relationship between the allocated bandwidth and the total delay, which established the adaptive control of the maximum number of retransmissions, thereby improving the performance of the URLLC. As delays exist by nature and play a critical part in affecting the freshness of information, the authors in [11] comprehensively considered various types of nontrivial system delays and derived unified closed-form average Age of Information (AoI) and average Peak AoI expressions for the HARQ. In order to support the new URLLC service, which aims to facilitate the transmission of small packets with strict requirements for latency and reliability, the authors in [12] proposed a spatiotemporal analytical framework for analyzing contention-based grant-free (GF) (i.e., configured grant) access schemes. It analyzed three GF access schemes with HARQ retransmissions, i.e., Reactive, K-repetition, and Proactive. It defined the latent access failure probability to characterize the URLLC reliability and latency performances. The results showed that under shorter latency constraints, the Proactive scheme provided the lowest latent access failure probability, whereas under longer latency constraints, the K-repetition GF transmission scheme achieved the lowest latent access failure probability. Although current research has enhanced the HARQ in various aspects, they have not considered the reasonable spatiotemporal allocation of transmission on limited channel resources based on real-time wireless conditions. This paper focuses on optimizing the HARQ scheme by addressing the delay and reliability of information transfer between 5G NR-V2X UEs. In order to reduce the average number of retransmissions, transmission delay, and average BLER, and to improve the reliability of information transmission, an AdaptiveHARQ transmission scheme is designed. This scheme includes \(T\)-delay, \(K\)-repetition, and [T, K]-overlap methods according to the factors that affect wireless conditions. Finally, we consider the external wireless channel conditions for adopting the Q-learning algorithm, with optimization based on reinforcement learning for improving the transmission of HARQ packets.