Heterogeneous UPF Integration Framework and 5G User Plane Acceleration.
APNOMS(2023)
Department of Computer Science
Abstract
The 3rd Generation Partnership Project (3GPP) proposes User Plane Function (UPF) in the fifth generation (5G) mobile networks to handle user data between radio access network (RAN) and data network (DN). Since pure software-based UPF does not fulfill the diversified service requirements of 5G networks, researchers have leveraged various hardware acceleration techniques to enhance the performance of UPF. However, different hardware implementations require different installations and adaptations. This paper proposes a framework to integrate various UPF implementations. It is compliant with standard UPF but decouples the control plane function (UPF-CP) from the user plane function (UPF-UP). We implemented UPF-CP by Packet Forwarding Control Protocol (PFCP) Agent with two implementations of UPF-UP: one with Intel Data Plane Development Kit (DPDK) and the other with Smart Network Interface Card (SmartNIC). We integrated the framework with an open-source 5G core network, free5GC, and evaluated the framework by experiments. The results confirm the interoperability of our work with free5GC and demonstrate the superiority of the hardware-accelerated UPF over pure software approach in terms of packet processing speed on the user plane.
MoreTranslated text
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined