Resource allocation for the LTE uplink based on Genetic Algorithms in mixed traffic environments

Long Term Evolution (LTE) is currently the main technology for the 4G networks. It aims to delivery unprecedented data rates and low latency for several types of applications. In this context, this paper investigates the resource allocation in the LTE uplink. From the principle that resource allocation in the uplink is a complex optimization problem, the main contribution of this paper is a novel scheduling algorithm based on Genetic Algorithms. This algorithm introduces new operations of initialization, crossover, mutation and a QoS-aware fitness function. The algorithm is evaluated in a mixed traffic environment and its performance is compared with relevant algorithms from the literature. Simulations were carried out in ns-3 and the results show that the proposed algorithm is able to meet the Quality of Service (QoS) requirements of the applications, offering a PSNR 13% higher than its main competitor, while presenting a satisfactory execution time of 1.5 times the execution of the simplest algorithm, the Round Robin.