20 citations · 41 across the 4 of their papers we have counts for
7 papers
A Bayesian Framework of Deep Reinforcement Learning for Joint O-RAN/MEC Orchestration
Fahri Wisnu Murti, Samad Ali, Matti Latva-aho
Multi-access Edge Computing (MEC) can be implemented together with Open Radio Access Network (O-RAN) over commodity platforms to offer low-cost deployment and bring the services cl…
Deep Reinforcement Learning for Orchestrating Cost-Aware Reconfigurations of vRANs
Fahri Wisnu Murti, Samad Ali, George Iosifidis +1
Virtualized Radio Access Networks (vRANs) are fully configurable and can be implemented at a low cost over commodity platforms to enable network management flexibility. In this pap…
Learning-Based Orchestration for Dynamic Functional Split and Resource Allocation in vRANs
Fahri Wisnu Murti, Samad Ali, George Iosifidis +1
One of the key benefits of virtualized radio access networks (vRANs) is network management flexibility. However, this versatility raises previously-unseen network management challe…
Constrained Deep Reinforcement Based Functional Split Optimization in Virtualized RANs
Fahri Wisnu Murti, Samad Ali, Matti Latva-aho
In virtualized radio access network (vRAN), the base station (BS) functions are decomposed into virtualized components that can be hosted at the centralized unit or distributed uni…
Deep Reinforcement Based Optimization of Function Splitting in Virtualized Radio Access Networks
Fahri Wisnu Murti, Samad Ali, Matti Latva-aho
Virtualized Radio Access Network (vRAN) is one of the key enablers of future wireless networks as it brings the agility to the radio access network (RAN) architecture and offers de…
On the Optimization of Multi-Cloud Virtualized Radio Access Networks
Fahri Wisnu Murti, Andres Garcia-Saavedra, Xavier Costa-Perez +1
We study the important and challenging problem of virtualized radio access network (vRAN) design in its most general form. We develop an optimization framework that decides the num…