Cyrus+: A DRL-based Puncturing Solution to URLLC/eMBB Multiplexing in O-RAN
Ehsan Ghoreishi, Bahman Abolhassani, Yan Huang, Shiva Acharya, Wenjing Lou, Y. Thomas Hou

TL;DR
Cyrus+ employs deep reinforcement learning and receiver feedback to optimize puncturing in 5G NR, effectively balancing URLLC and eMBB traffic in O-RAN architecture with improved performance over benchmarks.
Contribution
This paper introduces Cyrus+, a novel DRL-based puncturing method that uses goodput feedback and leverages O-RAN control loops for enhanced multiplexing of URLLC and eMBB.
Findings
Cyrus+ outperforms benchmark algorithms in simulations.
It effectively balances URLLC latency and eMBB throughput.
The approach meets 5G NR timing requirements.
Abstract
Puncturing is a promising technique in 3GPP to multiplex Enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low Latency Communications (URLLC) traffic on the same 5G New Radio (NR) air interface. The essence of puncturing is to transmit URLLC packets on demand upon their arrival, by preempting the radio resources (or subcarriers) that are already allocated to eMBB traffic. Although it is considered most bandwidth efficient, puncturing URLLC data on eMBB can lead to degradation of eMBB's performance. Most of the state-of-the-art research addressing this problem employ raw eMBB data throughput as performance metric. This is inadequate as, after puncturing, eMBB data may or may not be successfully decoded at its receiver. This paper presents Cyrus+, a deep reinforcement learning (DRL)-based puncturing solution that employs goodput (through feedback from a receiver's decoder), rather than…
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Taxonomy
TopicsDNA and Biological Computing · Speech Recognition and Synthesis · IPv6, Mobility, Handover, Networks, Security
