University of Oulu

F. W. Murti, S. Ali and M. Latva-aho, "Deep Reinforcement Based Optimization of Function Splitting in Virtualized Radio Access Networks," 2021 IEEE International Conference on Communications Workshops (ICC Workshops), 2021, pp. 1-6, doi: 10.1109/ICCWorkshops50388.2021.9473703

Deep reinforcement based optimization of function splitting in virtualized radio access networks

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Author: Murti, Fahri Wisnu1; Ali, Samad1; Latva-aho, Matti1
Organizations: 1Centre for Wireless Communications, University of Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.9 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2021
Publish Date: 2021-10-21


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 degrees of design freedom. Yet, it also creates a challenging problem on how to design the functional split configuration. In this paper, a deep reinforcement learning approach is proposed to optimize function splitting in vRAN. A learning paradigm is developed that optimizes the location of functions in the RAN. These functions can be placed either at a central/cloud unit (CU) or a distributed unit (DU). This problem is formulated as constrained neural combinatorial reinforcement learning to minimize the total network cost. In this solution, a policy gradient method with Lagrangian relaxation is applied that uses a stacked long short-term memory (LSTM) neural network architecture to approximate the policy. Then, a sampling technique with a temperature hyperparameter is applied for the inference process. The results show that our proposed solution can learn the optimal function split decision and solve the problem with a 0.4% optimality gap. Moreover, our method can reduce the cost by up to 320% compared to a distributed-RAN (D-RAN). We also conclude that altering the traffic load and routing cost does not significantly degrade the optimality performance.

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Series: IEEE International Conference on Communications workshop
ISSN: 2164-7038
ISSN-E: 2694-2941
ISSN-L: 2164-7038
ISBN: 978-1-7281-9441-7
ISBN Print: 978-1-7281-9442-4
Article number: 9473703
DOI: 10.1109/ICCWorkshops50388.2021.9473703
Host publication: 2021 IEEE International Conference on Communications Workshops (ICC Workshops)
Conference: IEEE International Conference on Communications Workshops
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work was supported by the Academy of Finland 6Genesis Flagship (grant no. 318927).
Academy of Finland Grant Number: 318927
Detailed Information: 318927 (Academy of Finland Funding decision)
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