Two-timescale Resource Allocation for Automated Networks in IIoT

TL;DR

This paper proposes a two-timescale resource allocation framework for IIoT networks with hybrid energy, using Lyapunov optimization and ADMM to optimize energy management, rate control, channel selection, and power allocation.

Contribution

It introduces an online, low-complexity solution for joint energy and rate control in IIoT with hybrid energy, addressing different timescale variations.

Findings

The algorithm guarantees bounded performance deviation.

Simulation results validate effectiveness across various configurations.

The approach accelerates convergence in resource allocation.

Abstract

The rapid technological advances of cellular technologies will revolutionize network automation in industrial internet of things (IIoT). In this paper, we investigate the two-timescale resource allocation problem in IIoT networks with hybrid energy supply, where temporal variations of energy harvesting (EH), electricity price, channel state, and data arrival exhibit different granularity. The formulated problem consists of energy management at a large timescale, as well as rate control, channel selection, and power allocation at a small timescale. To address this challenge, we develop an online solution to guarantee bounded performance deviation with only causal information. Specifically, Lyapunov optimization is leveraged to transform the long-term stochastic optimization problem into a series of short-term deterministic optimization problems. Then, a low-complexity rate control algorithm is developed based on alternating direction method of multipliers (ADMM), which accelerates the convergence speed via the decomposition-coordination approach. Next, the joint channel selection and power allocation problem is transformed into a one-to-many matching problem, and solved by the proposed price-based matching with quota restriction. Finally, the proposed algorithm is verified through simulations under various system configurations.