Understanding 5G Performance on Heterogeneous Computing Architectures

Virtualized radio access network uses virtualization technology to enable multiple network modalities (e.g., different network services and QoS levels) to run on general-purpose computing devices instead of proprietary hardware. These devices include diverse types such as CPUs, GPUs, and DSPs. However, there is a lack of comprehensive performance comparisons across these heterogeneous computing architectures. This article aims to fill this gap by investigating the performance impact of parallel computing, I/O operations, and task orchestration on vRAN efficiency. Through empirical analysis, we explore the performance and architectural differences between CPU-based, GPU-based, and DSP-based solutions such as Intel FlexRAN, NVIDIA Aerial, and Marvell Octeon. Our study provides insights for optimizing 5G performance, and offers strategic recommendations for deployment tailored to different operational goals. We also outline future directions for research and development in vRAN architecture optimization.

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Publication Details

Journal
IEEE Communications Magazine
Published
2024-10-22
DOI
https://doi.org/10.1109/mcom.001.2400267
Citations
1
Primary Topic
Molecular Communication and Nanonetworks
Type
article
Field-Weighted Citation Impact
0.18
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article

Understanding 5G Performance on Heterogeneous Computing Architectures

Chenren Xu, Weiguo Wang, Hao Nie, Shunmin Zhu et al.
1 citations
IEEE Communications Magazine
Molecular Communication and Nanonetworks
0.18
article

Understanding 5G Performance on Heterogeneous Computing Architectures

Chenren Xu, Weiguo Wang, Hao Nie, Shunmin Zhu, Junchen Guo, Pengyu Zhang, Chengke Wang, Yuan He, Yulong Chen
article en
1 citations

Abstract

Virtualized radio access network uses virtualization technology to enable multiple network modalities (e.g., different network services and QoS levels) to run on general-purpose computing devices instead of proprietary hardware. These devices include diverse types such as CPUs, GPUs, and DSPs. However, there is a lack of comprehensive performance comparisons across these heterogeneous computing architectures. This article aims to fill this gap by investigating the performance impact of parallel computing, I/O operations, and task orchestration on vRAN efficiency. Through empirical analysis, we explore the performance and architectural differences between CPU-based, GPU-based, and DSP-based solutions such as Intel FlexRAN, NVIDIA Aerial, and Marvell Octeon. Our study provides insights for optimizing 5G performance, and offers strategic recommendations for deployment tailored to different operational goals. We also outline future directions for research and development in vRAN architecture optimization.

IEEE Communications MagazineVol. 63(3)
Peking University (CN), Cloud Computing Center (CN), Alibaba Group (China) (CN), Tsinghua University (CN)
Openalex Percentile: Top 51%
Molecular Communication and Nanonetworks
0.18
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Understanding 5G Performance on Heterogeneous Computing Architectures — Chenren Xu, Weiguo Wang, et al. · IEEE Communications Magazine (2024) | TGRS Research Map | TGRS