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.
Highlights
Orthogonal frequency division multiple access (OFDMA) has emerged as one of the most promising schemes for broadband wireless networks
This paper introduces a novel joint channel and queuing-aware OFDMA scheduler for delay-sensitive traffic based on a hopfield neural network (HNN) scheme
This paper has presented a novel strategy to carry out the dynamic resource allocation of subcarriers to users in OFDMA systems with delay-sensitive service in which packets should be transmitted within a specific maximum delay bound
Summary
Orthogonal frequency division multiple access (OFDMA) has emerged as one of the most promising schemes for broadband wireless networks. Under the proper conditions we can take advantage of the fact that the so-called HNN energy evolves toward a minimum value [17] providing a final neuron state that includes, in a natural way, the optimal subcarrier combination to be allocated This optimal allocation can be obtained by properly including different constraints (i.e., channel and queue status for the different users) in the definition of the HNN energy. From an implementation point of view, HNN methodology can be carried out either by solving iteratively a numerical differential equation based on the Euler technique or by means of hardware implementations (HNN is derived with an initial hardware implementation in mind) such as the field-programmable gate array (FPGA) chip [18] that has been proved practically for implementation purposes Under this framework, this paper proposes a novel HNNbased joint channel and queue-aware scheduling strategy for downlink OFDMA systems suitable for delay-bounded services.
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