An Open, Programmable, Multi-vendor 5G O-RAN Testbed with NVIDIA ARC and OpenAirInterface

被引:1
|
作者
Villa, Davide [1 ]
Khan, Imran [1 ]
Kaltenberger, Florian [1 ,2 ]
Hedberg, Nicholas [3 ]
da Silva, Ruben Soares [4 ]
Kelkar, Anupa [3 ]
Dick, Chris [3 ]
Basagni, Stefano [1 ]
Jornet, Josep M. [1 ]
Melodia, Tommaso [1 ]
Polese, Michele [1 ]
Koutsonikolas, Dimitrios [1 ]
机构
[1] Northeastern Univ, Inst Wireless Internet Things, Boston, MA 02115 USA
[2] Eurecom, Sophia Antipolis, France
[3] NVIDIA Inc, Santa Clara, CA USA
[4] Allbesmart, Castelo Branco, Portugal
基金
美国国家科学基金会;
关键词
Private; 5G; GPU offloading; O-RAN;
D O I
10.1109/INFOCOMWKSHPS61880.2024.10620908
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
The transition of fifth generation (5G) cellular systems to softwarized, programmable, and intelligent networks depends on successfully enabling public and private 5G deployments that are (i) fully software-driven and (ii) with a performance at par with that of traditional monolithic systems. This requires hardware acceleration to scale the Physical (PHY) layer performance, end-to-end integration and testing, and careful planning of the Radio Frequency (RF) environment. In this paper, we describe how the X5G testbed at Northeastern University has addressed these challenges through the first 8-node network deployment of the NVIDIA Aerial RAN CoLab (ARC), with the Aerial Software Development Kit (SDK) for the PHY layer, accelerated on Graphics Processing Unit (GPU), and through its integration with higher layers from the OpenAirInterface (OAI) open-source project through the Small Cell Forum (SCF) Functional Application Platform Interface (FAPI). We discuss software integration, the network infrastructure, and a digital twin framework for RF planning. We then profile the performance with up to 4 Commercial Off-the-Shelf (COTS) smartphones for each base station with iPerf and video streaming applications, measuring a cell rate higher than 500 Mbps in downlink and 45 Mbps in uplink.
引用
收藏
页数:6
相关论文
共 50 条
  • [41] Practical Load Balancing Algorithm for 5G Small Cell Networks Based on Real-World 5G Traffic and O-RAN Architecture
    Cho, Young-Jun
    Yoo, Hyeon-Min
    Kim, Kyung-Sook
    Na, Jeehyeon
    Hong, Een-Kee
    IEEE ACCESS, 2024, 12 : 121947 - 121957
  • [42] ORAN-Sense: Localizing Non-cooperative Transmitters with Spectrum Sensing and 5G O-RAN
    Lizarribar, Yago
    Calvo-Palomino, Roberto
    Scalingi, Alessio
    Santaromita, Giuseppe
    Bovet, Gerome
    Giustiniano, Domenico
    IEEE INFOCOM 2024-IEEE CONFERENCE ON COMPUTER COMMUNICATIONS, 2024, : 1870 - 1879
  • [43] 中国联通黄蓉 O-RAN为5G大规模部署探路
    梅雅鑫
    通信世界, 2019, (10) : 38 - 38
  • [44] 基于AI和O-RAN架构的5G网络容量自适应算法
    郑康
    段然
    吴杰
    袁宇恒
    电信工程技术与标准化, 2020, 33 (01) : 19 - 24
  • [45] O-RAN 5G小基站在工业园区网络的应用
    蔡子华
    黄劲安
    林东云
    广东通信技术, 2021, 41 (04) : 42 - 45
  • [46] 5G O-RAN小基站:繁荣应用场景降低产业成本
    段然
    崔春风
    通信世界, 2020, (03) : 23 - 24
  • [47] Antenna-in-Packages for Array Modularization at Millimeter-wave Frequencies and its Applications in 5G O-RAN
    Chou, Hsi-Tseng
    Wu, Kuan-Hsun
    Lin, Zhao-He
    Yan, Zhi-Da
    Lin, Ding-Bing
    2021 INTERNATIONAL SYMPOSIUM ON ANTENNAS AND PROPAGATION (ISAP), 2021,
  • [48] Cell-Free mMIMO Support in the O-RAN Architecture: A PHY Layer Perspective for 5G and Beyond Networks
    Ranjbar V.
    Girycki A.
    Rahman M.A.
    Pollin S.
    Moonen M.
    Vinogradov E.
    IEEE Communications Standards Magazine, 2022, 6 (01): : 28 - 34
  • [49] 恩智浦O-RAN 5G小基站为垂直行业“量体裁衣”
    程琳琳
    王鹤迦
    通信世界, 2023, (07) : 37 - 37
  • [50] Deep Autoencoder Design for RF Anomaly Detection in 5G O-RAN Near-RT RIC via xApps
    Basaran, Osman Tugay
    Basaran, Mehmet
    Turan, Derya
    Bayrak, Hamide Gul
    Sandal, Yagmur Sabucu
    2023 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS WORKSHOPS, ICC WORKSHOPS, 2023, : 549 - 555