Federated Learning Assisted Deep Q-Learning for Joint Task Offloading and Fronthaul Segment Routing in Open RAN

TL;DR

This paper proposes a federated learning-assisted deep Q-learning approach to optimize joint task offloading and fronthaul routing in Open RAN, reducing delays and improving resource utilization in 5G networks.

Contribution

It introduces a novel joint optimization framework for task offloading and routing in Open RAN using federated deep Q-learning, addressing a previously unstudied problem.

Findings

The proposed method effectively minimizes task offloading and routing delays.

Simulation results demonstrate improved reward and reduced cost of delay.

The approach outperforms baseline methods in delay reduction.

Abstract

Offloading computation-intensive tasks to edge clouds has become an efficient way to support resource constraint edge devices. However, task offloading delay is an issue largely due to the networks with limited capacities between edge clouds and edge devices. In this paper, we consider task offloading in Open Radio Access Network (O-RAN), which is a new 5G RAN architecture allowing Open Central Unit (O-CU) to be co-located with Open Distributed Unit (DU) at the edge cloud for low-latency services. O-RAN relies on fronthaul network to connect O-RAN Radio Units (O-RUs) and edge clouds that host O-DUs. Consequently, tasks are offloaded onto the edge clouds via wireless and fronthaul networks \cite{10045045}, which requires routing. Since edge clouds do not have the same available computation resources and tasks' computation deadlines are different, we need a task distribution approach to multiple edge clouds. Prior work has never addressed this joint problem of task offloading, fronthaul routing, and edge computing. To this end, using segment routing, O-RAN intelligent controllers, and multiple edge clouds, we formulate an optimization problem to minimize offloading, fronthaul routing, and computation delays in O-RAN. To determine the solution of this NP-hard problem, we use Deep Q-Learning assisted by federated learning with a reward function that reduces the Cost of Delay (CoD). The simulation results show that our solution maximizes the reward in minimizing CoD.