Utility Optimal Scheduling with a Slow Time-Scale Index-Bias for Achieving Rate Guarantees in Cellular Networks

TL;DR

This paper introduces a three-time-scale scheduling algorithm for 5G network slicing that ensures rate guarantees, improves upon prior methods, and stabilizes user equipment rates near optimal levels through a novel index-bias approach.

Contribution

The paper proposes a new three-time-scale algorithm with index-bias updates for rate-guaranteed scheduling, enhancing stability and performance over previous two-time-scale methods.

Findings

UE rates stabilize near the optimal operating point.

Index-biases fluctuate minimally around Lagrange multipliers.

The algorithm outperforms prior two-time-scale approaches.

Abstract

One of the requirements of network slicing in 5G networks is RAN (radio access network) scheduling with rate guarantees. We study a three-time-scale algorithm for maximum sum utility scheduling, with minimum rate constraints. As usual, the scheduler computes an index for each UE in each slot, and schedules the UE with the maximum index. This is at the fastest, natural time-scale of channel fading. The next time-scale is of the exponentially weighted moving average (EWMA) rate update. The slowest time scale in our algorithm is an "index-bias" update by a stochastic approximation algorithm, with a step-size smaller than the EWMA. The index-biases are related to Lagrange multipliers, and bias the slot indices of the UEs with rate guarantees, promoting their more frequent scheduling. We obtain a pair of coupled ordinary differential equations (o.d.e.) such that the unique stable points of the two o.d.e.s are the primal and dual solutions of the constrained utility optimization problem. The UE rate and index-bias iterations track the asymptotic behaviour of the o.d.e. system for small step-sizes of the two slower time-scale iterations. Simulations show that, by running the index-bias iteration at a slower time-scale than the EWMA iteration and using the EWMA throughput itself in the index-bias update, the UE rates stabilize close to the optimum operating point on the rate region boundary, and the index-biases have small fluctuations around the optimum Lagrange multipliers. We compare our results with a prior two-time-scale algorithm and show improved performance.