A 5G OpenAirInterface (OAI) Testbed with MEC: Deployment, Application testing and Slicing Support

Thakkar, Priyal; Sanadhya, Shashvat; Gandotra, Pimmy; Lall, Brejesh

doi:10.1109/comsnets56262.2023.10041332

Cited by 7 publications

(2 citation statements)

References 5 publications

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“…The experimental system included a gNB and 6 5G mobile phones, as illustrated in Figure 10. The gNB was built with a USRP B210 and the OAI platform [36,37], and the specific parameters are listed in Table 1. The 5G core network was also established using OAI, with Docker containers and Docker Compose to facilitate the installation and management.…”

Section: Experimental Set-up and Data Collectionmentioning

confidence: 99%

Radio Frequency Fingerprint Identification for 5G Mobile Devices Using DCTF and Deep Learning

Fu,

Dong,

Yin

et al. 2023

Entropy

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The fifth-generation (5G) mobile cellular network is vulnerable to various security threats. Radio frequency fingerprint (RFF) identification is an emerging physical layer authentication technique which can be used to detect spoofing and distributed denial of service attacks. In this paper, the performance of RFF identification is studied for 5G mobile phones. The differential constellation trace figure (DCTF) is extracted from the physical random access channel (PRACH) preamble. When the database of all 64 PRACH preambles is available at the gNodeB (gNB), an index-based DCTF identification scheme is proposed, and the classification accuracy reaches 92.78% with a signal-to-noise ratio of 25 dB. Moreover, due to the randomness in the selection of preamble sequences in the random access procedure, when only a portion of the preamble sequences can be trained, a group-based DCTF identification scheme is proposed. The preamble sequences generated from the same root value are grouped together, and the untrained sequences can be identified based on the trained sequences within the same group. The classification accuracy of the group-based scheme is 89.59%. An experimental system has been set up using six 5G mobile phones of three models. The 5G gNB is implemented on the OpenAirInterface platform.

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Section: Experimental Set-up and Data Collectionmentioning

confidence: 99%

Radio Frequency Fingerprint Identification for 5G Mobile Devices Using DCTF and Deep Learning

Fu,

Dong,

Yin

et al. 2023

Entropy

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“…There has been some work on the application and testing of the OAI platform. The reference provides an overview of the OAI project and its current state-of-the-art of 5G implementation efforts 1 . The work describes how to build a 5G New Radio Standalone (NR SA) system based on the OAI platform and universal software radio peripheral (USRP) devices 2 .…”

Section: Introductionmentioning

confidence: 99%

Fundamental characteristics and arrival curve modeling of live streaming traffic via 5G NR system based on OAI platform

Gao,

Kuerban,

Wang

et al. 2023

Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023)

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In order to better guarantee different quality of service requirements for different types of 5G network traffic, the characteristics should be studied and modeled for each type of traffic carried via 5G network. In this paper, we report a measurement-based study, which includes 5G network traffic data collection, analysis and modeling. To better extract the behavioral characteristics of 5G network traffic, we conduct the collection of traffic data from the 5G new radio (NR) system based on OpenAirInterface (OAI) platform and universal software radio peripheral (USRP). In this paper, three types of traffic carried via 5G network are collected and analyzed. For basic traffic characteristics, the traffic rates and the probability density distribution of packet length and packet interval are analyzed. For traffic modeling, the concept of cumulative arrival process is adopted and the arrival curve model is derived based on network calculus.

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5G Testbed as a Service (5GTaaS): An Open Source Implementation for Networks of the Future

Reddy,

Singh,

Lipps

et al. 2023

2023 IEEE Future Networks World Forum (FNWF)

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A 5G OpenAirInterface (OAI) Testbed with MEC: Deployment, Application testing and Slicing Support

Cited by 7 publications

References 5 publications

Radio Frequency Fingerprint Identification for 5G Mobile Devices Using DCTF and Deep Learning

Radio Frequency Fingerprint Identification for 5G Mobile Devices Using DCTF and Deep Learning

Fundamental characteristics and arrival curve modeling of live streaming traffic via 5G NR system based on OAI platform

5G Testbed as a Service (5GTaaS): An Open Source Implementation for Networks of the Future

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