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