Measurement Study of Programmable Network Coding in Cloud-native 5G and Beyond Networks
<|reference_start|>Measurement Study of Programmable Network Coding in Cloud-native 5G and Beyond Networks: Emerging 5G/6G use cases span various industries, necessitating flexible solutions that leverage emerging technologies to meet diverse and stringent application requirements under changing network conditions. The standard 5G RAN solution, retransmission, reduces packet loss but can increase transmission delay in the process. Random Linear Network Coding (RLNC) offers an alternative by proactively sending combinations of original packets, thus reducing both delay and packet loss. Current research often only simulates the integration of RLNC in 5G while we implement and evaluate our approach on real commercially available hardware in a real-world deployment.   We introduce Flexible Network Coding (FlexNC), which enables the flexible fusion of several RLNC protocols by incorporating a forwarder with multiple RLNC nodes. Network operators can configure FlexNC based on network conditions and application requirements. To further boost network programmability, our Recoder in the Network (RecNet) leverages intermediate network nodes to join the coding process. Both the proposed algorithms have been implemented on OpenAirInterface and extensively tested with traffic from different applications in a real network. While FlexNC adapts to various application needs of latency and packet loss, RecNet significantly minimizes packet loss for a remote user with minimal increase in delay compared to pure RLNC.<|reference_end|>
@article{lhamo2024measurement,
    title={Measurement Study of Programmable Network Coding in Cloud-native 5G and
  Beyond Networks},
    author={Osel Lhamo, Tung V. Doan, Elif Tasdemir, Mahdi Attawna, Giang T.
  Nguyen, Patrick Seeling, Martin Reisslein, Frank H.P. Fitzek},
    journal={arXiv preprint arXiv:2408.06115},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.06115},
    primaryClass={cs.NI}
}