End-to-End O-RAN Control-Loop For Radio Resource Allocation in SDR-Based 5G Network

被引：1

作者：

Tripathi, Asheesh ^{[1
]}

Mallu, Jaswanth S. R. ^{[1
]}

Rahman, Md. Habibur ^{[1
]}

Sultana, Abida ^{[1
]}

Sathish, Aditya ^{[1
]}

Huff, Alexandre ^{[2
]}

Chowdhury, Mayukh Roy ^{[1
]}

da Silva, Aloizio Pereira ^{[1
]}

机构：

[1] Virginia Tech, Commonwealth Cyber Initiat, Arlington, VA 22203 USA

[2] Fed Technol Univ Parana, Curitiba, Parana, Brazil

来源：

MILCOM 2023 - 2023 IEEE MILITARY COMMUNICATIONS CONFERENCE | 2023年

关键词：

ORAN; srsRAN; Non-RT RIC; Near-RT RIC; AI/ML framework; 5G NR SA;

D O I：

10.1109/MILCOM58377.2023.10356316

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

End-to-end Open Radio Access Network (ORAN) demos, including Software-defined Radios (SDRs), can identify key gaps in the early stages of research and accelerate time-to-market and early adoption for next-generation wireless technologies. This work showcases an innovative end-to-end 5G-based ORAN deployment that leverages open-source tools and an Artificial Intelligence (AI)/Machine Learning (ML) framework. The deployment utilizes Software Radio Systems RAN (srsRAN) Central Unit (CU)-Distributed Unit (DU) connected to the Open5GS core network operating in 5G standalone (SA) mode. It also demonstrates the integration of Non-Real Time RIC (Non-RT RIC) and Near-Real Time RIC (Near-RT RIC) with embedded intelligence to perform radio resource allocation. An AI/ML framework deploys an optimized ML model as an rApp that complements a resource allocation xApp.

引用

页数：2

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