Joint Link Adaptation and Device Scheduling Approach for URLLC Industrial IoT Network: A DRL-based Method with Bayesian Optimization

TL;DR

This paper introduces a DRL-based joint link adaptation and device scheduling method for URLLC in industrial IoT networks, effectively handling imperfect CSI and improving convergence speed and sum-rate performance.

Contribution

It proposes a Bayesian optimization driven TD3 algorithm with a BO-based training mechanism to enhance convergence and reliability in URLLC device scheduling.

Findings

Faster convergence compared to existing methods.

Higher sum-rate performance in simulations.

Effective handling of imperfect CSI and sample imbalance.

Abstract

In this article, we consider an industrial internet of things (IIoT) network supporting multi-device dynamic ultra-reliable low-latency communication (URLLC) while the channel state information (CSI) is imperfect. A joint link adaptation (LA) and device scheduling (including the order) design is provided, aiming at maximizing the total transmission rate under strict block error rate (BLER) constraints. In particular, a Bayesian optimization (BO) driven Twin Delayed Deep Deterministic Policy Gradient (TD3) method is proposed, which determines the device served order sequence and the corresponding modulation and coding scheme (MCS) adaptively based on the imperfect CSI. Note that the imperfection of CSI, error sample imbalance in URLLC networks, as well as the parameter sensitivity nature of the TD3 algorithm likely diminish the algorithm's convergence speed and reliability. To address such an issue, we proposed a BO based training mechanism for the convergence speed improvement, which provides a more reliable learning direction and sample selection method to track the imbalance sample problem. Via extensive simulations, we show that the proposed algorithm achieves faster convergence and higher sum-rate performance compared to existing solutions.