Elastic O-RAN Slicing for Industrial Monitoring and Control: A
Distributed Matching Game and Deep Reinforcement Learning Approach
Abstract
In this work, we design an elastic open radio access network (O-RAN)
slicing for the industrial Internet of things (IIoT). Unlike IoT, IIoT
poses additional challenges such as severe communication environment,
network-slice resource demand variations, and on-time information update
from the IIoT devices during industrial production. First, we formulate
the O-RAN slicing problem for on-time industrial monitoring and control
where the objective is to minimize the cost of fresh information updates
(i.e., age of information (AoI)) from the IIoT devices (i.e., sensors)
while maintaining the energy consumption of those devices with the
energy constraint as well as O-RAN slice isolation constraints. Second,
we propose the intelligent ORAN framework based on game theory and
machine learning to mitigate the problem’s complexity. We propose a
two-sided distributed matching game in the O-RAN control layer that
captures the IIoT channel characteristics and the IIoT service
priorities to create IIoT device and small cell base station (SBS)
preference lists. We then employ an actor-critic model with a deep
deterministic policy gradient (DDPG) in the O-RAN service management
layer to solve the resource allocation problem for optimizing the
network slice configuration policy under time varying slicing demand.
While the matching game helps the actor-critic model, the DDPG enforces
the long-term policy-based guidance for resource allocation that
reflects the trends of all IIoT devices and SBSs satisfactions with the
assignment. Finally, the simulation results show that the proposed
solution enhances the performance gain for the IIoT services by serving
an average of 50% and 43.64% more IIoT devices than the baseline
approaches.