A Chance Constrained Based Formulation for Dynamic Multiplexing of eMBB-URLLC Traffics in 5G New Radio

Abstract

5G New Radio (NR) is envisioned to provide three major services: enhanced Mobile Broadband (eMBB), Ultra Reliable Low Latency Communications (URLLC), and massive Machine Type Communication (mMTC). URLLC services (i.e., autonomous vehicles, industrial Internet of Things (IoT),…) require strict latency, on-way latency of 1 ms, with 99.999% reliability. eMBB applications aims extreme data rate while mMTC is designed to serve a large number of IoT devices that send a small data sporadically. In this paper, we address the resource scheduling problem of URLLC and eMBB traffics. First, the Resource Blocks (RBs) are allocated to eMBB users at the beginning of each time slot based on the channel state of each eMBB user and his previous average data rate up to current time slot. The RBs allocation problem modeled as as a 2-Dimensions Hopfield Neural Networks (2D-HNN) and the energy function of 2D-HNN is investigated to solve the RBs allocation problem. Then, the resource scheduling problem of URLLC and eMBB is formulated as an optimization problem with chance constraint. The chance constraint based problem aims to maximize the eMBB data rate while satisfying the URLLC critical constraints. The cumulative Distribution Function (CDF) of the stochastic URLLC traffic is investigated to relax the chance constraint into a linear constraint. The simulation results show efficiency of the proposed dynamic scheduling approach.