Low Complexity Fair Scheduling in LTE/LTE-A Uplink Involving Multiple Traffic Classes

TL;DR

This paper proposes a low-complexity, joint channel and buffer aware uplink scheduling algorithm for LTE/LTE-A that supports mixed traffic with delay constraints, using delay outage minimization and the Hungarian algorithm.

Contribution

It introduces a novel uplink scheduling method that incorporates delay constraints and mixed traffic requirements with reduced computational complexity.

Findings

Supports real-time traffic with delay constraints

Minimizes bandwidth starvation for lower priority traffic

Achieves optimal resource allocation efficiently

Abstract

The bulk of the research on Long Term Evolution/Long Term Evolution-Advanced packet scheduling is concentrated in the downlink and the uplink is comparatively less explored. In up-link, channel aware scheduling with throughput maximization has been widely studied while considering an infinitely back-logged buffer model, which makes the investigations unrealistic. Therefore, we propose an optimal uplink packet scheduling pro-cedure with realistic traffic sources. Firstly, we advocate a joint channel and buffer aware algorithm, which maximizes the actual transmitted bit-count. Thereafter, we introduce delay constraints in our algorithm to support real-time traffic. We further enhance our algorithm by incorporating the varied delay and throughput requirements demanded by mixed traffic classes. Finally, we in-troduce priority flipping to minimize bandwidth starvation of lower priority traffic in presence of higher percentage of high priority traffic. We observe that a delay constraint may render the optimization-based proposals infeasible. Therefore, to avoid infeasibility, we replace the delay constraint with delay outage minimization (DOM). DOM aims at minimizing the packet drop due to delay violation. Moreover, DOM also helps in reducing the problems to a well-known assignment problem, which can be solved by applying the Hungarian algorithm. Hence, our approach delivers an optimal allocation with low computational complexity.