A New OFDMA Scheduler for Delay-Sensitive Traffic Based on Hopfield Neural Networks

Abstract

This paper introduces a novel joint channel and queuing-aware OFDMA scheduler for delay-sensitive traffic based on a hopfield neural network (HNN) scheme. It allows providing an optimum OFDMA performance by solving a complex combinational problem. The algorithm is based on distributing the available subcarriers among the users depending, on the one hand, on the time left for the transmission of the different packets in due time, so that packet droppings are avoided. On the other hand, it also accounts for the available channel capacity in each subcarrier depending on the channel status reported by the different users. The different requirements are captured in the form of an energy function that is minimized by the algorithm. In that respect, the paper illustrates two different algorithms coming from two settings of this energy function. The algorithms have been evaluated for delay-sensitive traffic and they have been compared against other state-of-the-art algorithms existing in the literature, exhibiting a better behavior in terms of packet-dropping probability.

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