Knowledge-Assisted Deep Reinforcement Learning in 5G Scheduler Design: From Theoretical Framework to Implementation

Abstract

In this paper, we develop a knowledge-assisted deep reinforcement learning (DRL) algorithm to design wireless schedulers in the fifth-generation (5G) cellular networks with time-sensitive traffic. Since the scheduling policy is a deterministic mapping from channel and queue states to scheduling actions, it can be optimized by using deep deterministic policy gradient (DDPG). We show that a straightforward implementation of DDPG converges slowly, has a poor quality-of-service (QoS) performance, and cannot be implemented in real-world 5G systems, which are non-stationary in general. To address these issues, we propose a theoretical DRL framework, where theoretical models from wireless communications are used to formulate a Markov decision process in DRL. To reduce the convergence time and improve the QoS of each user, we design a knowledge-assisted DDPG (K-DDPG) that exploits expert knowledge of the scheduler deign problem, such as the knowledge of the QoS, the target scheduling policy, and the importance of each training sample, determined by the approximation error of the value function and the number of packet losses. Furthermore, we develop an architecture for online training and inference, where K-DDPG initializes the scheduler off-line and then fine-tunes the scheduler online to handle the mismatch between off-line simulations and non-stationary real-world systems. Simulation results show that our approach reduces the convergence time of DDPG significantly and achieves better QoS than existing schedulers (reducing 30% ∼ 50% packet losses). Experimental results show that with off-line initialization, our approach achieves better initial QoS than random initialization and the online fine-tuning converges in few minutes.

A Risk-Sensitive Task Offloading Strategy for Edge Computing in Industrial Internet of Things

“…To characterize the fading channel in the practical automated factory, we set the channel distribution as a mixture of Rayleigh and log-normal distribution, which has been confirmed by the measurements in the real industrial environment [37]. The parameter of the Rayleigh distribution is set to be uniformly distributed in (0.5, 1) for each IIoT-ECS pair, and correspondingly, the two parameters of the log-normal distribution are set to be uniformly distributed in (1,2) and (0, 4) respectively. Finally, we set the confidence level to α = 0.99.…”

“…Intelligent factory automation is one of the typical applications envisioned in ultrareliable and low-latency communications (URLLC) scenarios in the fifth generation (5G) and the coming sixth generation (6G) communications [1,2]. In future smart factories, machines and sensors are seamlessly connected with each other through wireless links to conduct production tasks corporately.…”