| Full text | |
| Author(s): |
Lopes, Victor H. L.
;
Almeida, Gabriel M.
;
Klautau, Aldebaro
;
Cardoso, Kleber V.
Total Authors: 4
|
| Document type: | Journal article |
| Source: | ICC 2024 - IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS; v. N/A, p. 6-pg., 2024-01-01. |
| Abstract | |
Interference mitigation is a common benefit claimed by disaggregated and virtualized radio access networks (vRAN). However, this benefit depends on centralizing the proper virtual network functions (VNFs) from the protocol stack of neighbor radio units (RUs). Additionally, the available computing resources and dynamic demand in RUs must be taken into consideration to obtain efficient results. Naturally, this problem also appears in O-RAN infrastructures which motivates an approach that leverages the O-RAN architecture, including its machine learning-guided design. In this work, we formulate the problem as a Markovian decision process (MDP) and solve it by employing a deep reinforcement learning (DRL) agent. We also describe how our proposal can be implemented inside the O-RAN architecture. Through simulations, we show the improved spectral efficiency provided by the DRL agent while solving the complex VNF placement considering resource constraints, RUs vicinity, and dynamic demand. (AU) | |
| FAPESP's process: | 20/05127-2 - SAMURAI: smart 5G core and multiran integration |
| Grantee: | Aldebaro Barreto da Rocha Klautau Junior |
| Support Opportunities: | Research Projects - Thematic Grants |