A Novel Link Adaptation Approach for URLLC: A DRL-Based Method with OLLA

The strict block error rate (BLER) requirement under the time-varying nature of wireless channels in Ultra-reliable low-latency communication (URLLC) systems pose sig-nificant challenges for link adaptation (LA). To tackle these challenges, we propose a novel LA method that adaptively selects the modulation and coding scheme (MCS) without requiring perfect channel knowledge which is unrealistic to obtain in URLLC. The goal is to maximize the coding rate while ensuring strict BLER constraints in URLLC systems. To achieve this, we utilize the Deep Q-Network (DQN) algorithm to select the MCS dynamically. Furthermore, we enhance the MCS selection process by using the Outer Loop Link Adaptation algorithm for transmission reliability improvement. Given the nature of URLLC, the samples of ACK and NACK are highly imbalanced, which can cause issues in the training process. To address it, we propose a novel training mechanism that improves the performance of DQN model and convergence speed during the training stage. Through extensive simulations, we demonstrate that our proposed algorithm outperforms existing methods regarding coding rate and imposing strict BLER constraints.