How Should I Orchestrate Resources of My Slices for Bursty URLLC Service Provision?

TL;DR

This paper proposes a resource orchestration framework for bursty URLLC services in network slicing, ensuring low packet blocking and error probabilities through resource re-cutting, CoMP beamforming, and an optimized multiplexing strategy.

Contribution

It introduces a novel resource orchestration approach combining bandwidth re-cutting, CoMP channel use optimization, and a joint multiplexing algorithm with provable guarantees for bursty URLLC services.

Findings

Derived minimum bandwidth bounds for URLLC with low blocking probability.

Established minimum channel uses for URLLC with low error decoding probability.

Developed a distributed optimization algorithm with performance guarantees.

Abstract

Future wireless networks are convinced to provide flexible and cost-efficient services via exploiting network slicing techniques. However, it is challenging to configure network slicing systems for bursty ultra-reliable and low latency communications (URLLC) service provision due to its stringent requirements on low packet blocking probability and low codeword error decoding probability. In this paper, we propose to orchestrate network resources for a network slicing system to guarantee a more reliable bursty URLLC service provision. We re-cut physical resource blocks (PRBs) and derive the minimum upper bound of bandwidth for URLLC transmission with a low packet blocking probability. We correlate coordinated multipoint (CoMP) beamforming with channel uses and derive the minimum upper bound of channel uses for URLLC transmission with a low codeword error decoding probability. Considering the agreement on converging diverse services onto shared infrastructures, we further investigate the network slicing for URLLC and enhanced mobile broadband (eMBB) service multiplexing. Particularly, we formulate the service multiplexing as an optimization problem to maximize the long-term total slice utility. The mitigation of this problem is challenging due to the requirements of future channel information and tackling a two timescale issue. To address the challenges, we develop a joint resource optimization algorithm based on a sample average approximate (SAA) technique and a distributed optimization method with provable performance guarantees.