On-Demand Chaotic Neural Network for Broadcast Scheduling Problem

Authors:
Kushan Ahmadian;Marina Gavrilova
Affiliations:
Dept of Computer Science, University of Calgary,;Dept of Computer Science, University of Calgary,
Venue:
ICCSA '09 Proceedings of the International Conference on Computational Science and Its Applications: Part II
Year:
2009

Citing 5
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Abstract

An on-demand chaotic noise injection strategy for Broadcast Scheduling Problem (BSP) based on an adjacency matrix is described in this paper. Packet radio networks have many applications especially for military purposes while finding an optimized scheduling to transmit data is proven to be a NP-hard domain problem. The objective of the proposed method is to find an optimal time division multiple access (TDMA) frame based on maximizing channel utilization. The proposed method benefits from an on-demand noise injection policy which, unlike previous Noise Chaotic Neural Networks (NCNN) that suffers from blind injection policy, injects noise based on the status of neuron and its neighborhoods. The experimental result shows that in most cases the on-demand noise injection finds the best solution with minimal average time delays and maximum channel utilization in comparison to previous methods.