Resource Allocation in URLLC with Online Learning for Mobile Users

Neural networks (NNs) have been applied to solve various problems in ultra-reliable and low-latency communications (URLLC). Facing the stringent quality of service requirement, the time for training and running NNs is not negligible, and how to ensure the reliability with learning-based solutions is challenging, especially in a dynamic environment. In this paper, we propose an online learning method, which fine-tunes the NNs trained without supervision to ensure the reliability of URLLC for mobile users. A joint power and bandwidth allocation problem, aiming to minimize the bandwidth required for satisfying the quality of service of each user, is considered as an example. A "learning-to-optimize"method with offline training is provided for comparison. Simulation results show that the proposed online learning method can achieve comparable system performance as the offline training method, where the time consumed for online training and inference is about 25% of the 1 ms latency bound for the considered setup. Besides, the online learning method adapts to the abrupt change of average packet arrival rate quickly and can ensure reliability by setting the required overall packet loss probability conservative slightly. By contrast, the offline training method yields much worse reliability when the arrival rate varies.