云原生5G及未来网络中可编程网络编码的测量研究
Measurement Study of Programmable Network Coding in Cloud-native 5G and Beyond Networks
AI总结：
本文提出FlexNC和RecNet两种可编程网络编码方案，在真实5G硬件上实现并验证其能灵活适配应用需求并显著降低丢包。
摘要：
新兴的5G/6G用例横跨多个行业，需要灵活的解决方案，利用新兴技术在不断变化的网络条件下满足多样且严格的应用需求。标准5G无线接入网（RAN）方案中的重传机制虽然能减少丢包，但在此过程中可能增加传输时延。随机线性网络编码（Random Linear Network Coding, RLNC）通过主动发送原始数据包的组合来提供一种替代方案，从而同时降低时延和丢包。当前研究通常仅对RLNC在5G中的集成进行仿真，而我们则在真实商用硬件和实际部署环境中实现并评估了我们的方法。我们提出灵活网络编码（Flexible Network Coding, FlexNC），通过引入包含多个RLNC节点的转发器，实现多种RLNC协议的灵活融合。网络运营商可以根据网络条件和应用需求配置FlexNC。为进一步增强网络可编程性，我们提出的网络内重编码器（Recoder in the Network, RecNet）利用中间网络节点参与编码过程。所提出的两种算法均已在OpenAirInterface上实现，并在真实网络中利用来自不同应用的流量进行了广泛测试。FlexNC能够适应不同应用对时延和丢包的需求，而RecNet与纯RLNC相比，在时延增加极小的情况下显著降低了远端用户的丢包率。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
