Greedy-Knapsack Algorithm for Optimal Downlink Resource Allocation in LTE Networks

TL;DR

This paper introduces a greedy-knapsack algorithm for LTE downlink resource allocation that optimizes user scheduling to enhance throughput and QoS during overload conditions.

Contribution

It proposes a novel greedy-knapsack based scheduling algorithm with a class-based ranking function for improved LTE resource management.

Findings

High throughput, low loss, and reduced delay achieved

Effective under overload traffic conditions

Outperforms existing scheduling methods

Abstract

The Long Term Evolution (LTE) as a mobile broadband technology supports a wide domain of communication services with different requirements. Therefore, scheduling of all flows from various applications in overload states in which the requested amount of bandwidth exceeds the limited available spectrum resources is a challenging issue. Accordingly, in this paper, a greedy algorithm is presented to evaluate user candidates which are waiting for scheduling and select an optimal set of the users to maximize system performance, without exceeding available bandwidth capacity. The greedy-knapsack algorithm is defined as an optimal solution to the resource allocation problem, formulated based on the fractional knapsack problem. A compromise between throughput and QoS provisioning is obtained by proposing a class-based ranking function, which is a combination of throughput and QoS related parameters defined for each application. The simulation results show that the proposed method provides high performance in terms of throughput, loss and delay for different classes of QoS over the existing ones, especially under overload traffic.