NetMod: Toward Accelerating Cloud RAN Distributed Unit Modulation Within Programmable Switches
Radio Access Networks (RAN) are anticipated to gradually transition towards Cloud RAN (C-RAN), leveraging the full advantages of the cloud-native computing model. While this paradigm shift offers a promising architectural evolution to improve scalability, efficiency, and performance, significant challenges remain in managing the massive computing requirements of physical layer (PHY) processing. To address these challenges and meet the stringent Service Level Objectives (SLOs) in 5G networks, hardware acceleration technologies are essential. In this paper, we aim to mitigate this challenge by offloading 5G modulation mapping, a critical yet demanding function to encode bits into IQ symbols, directly onto the switch ASICs. Specifically, we introduce NetMod, a 5G New Radio (NR) standard-compliant in-network modulation mapper accelerator. NetMod leverages the capabilities of new-generation programmable switches within the C-RAN infrastructure to offload and accelerate PHY modulation functions. We implemented a NetMod prototype on a real-world platform using the Intel Tofino programmable switch and commodity servers running the Data Plane Development Kit (DPDK). Through extensive experiments, we demonstrate that NetMod achieves modulation mapping at switch line rate using minimal switch resources, thereby preserving ample space for traditional switching tasks. Furthermore, comparisons with a GPU-based 5G modulation mapper show that NetMod is 2.2<inline-formula><tex-math notation="LaTeX">$\boldsymbol{\times}$</tex-math></inline-formula> to 3.3<inline-formula><tex-math notation="LaTeX">$\boldsymbol{\times}$</tex-math></inline-formula> faster using only a single switch port. These results highlight the potential of in-network acceleration to enhance 5G network performance and efficiency.
Authors
- Abdulbary Naji (ORCID: https://orcid.org/0000-0001-6833-2018)
- Aiman Ghannami (ORCID: https://orcid.org/0000-0003-1671-1483)
- Liang Zhao (ORCID: https://orcid.org/0000-0001-5829-6850)
- Ammar Hawbani (ORCID: https://orcid.org/0000-0002-1069-3993)
- Xingfu Wang (ORCID: https://orcid.org/0000-0002-1301-3535)
- Xiaohua Xu (ORCID: https://orcid.org/0000-0001-7770-803X)
- Wei Zhao (ORCID: https://orcid.org/0000-0001-9799-4635)
Institutions
- Shenyang Aerospace University (CN)
- University of Science and Technology of China (CN)
- Eindhoven University of Technology (NL)
- Anhui University of Technology (CN)
Publication Details
- Journal
- IEEE Transactions on Computers
- Published
- 2024-11-20
- DOI
- https://doi.org/10.1109/tc.2024.3500379
- Citations
- 2
- Primary Topic
- Software-Defined Networks and 5G
- Type
- article
- Field-Weighted Citation Impact
- 0.70
Funders
- Shenyang Aerospace University
- University of Science and Technology of China