On-demand chaotic neural network for broadcast scheduling problem

Authors:
Marina Gavrilova;Kushan Ahmadian
Affiliations:
Dept of Computer Science, University of Calgary, Calgary, Canada;Dept of Computer Science, University of Calgary, Calgary, Canada
Venue:
The Journal of Supercomputing
Year:
2012

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Abstract

This paper presents a novel approach to optimizing network packet transfer scheme through introducing a new method for on-demand chaotic noise injection strategy for the Broadcast Scheduling Problem (BSP). Packet radio networks have many applications, while finding an optimized scheduling to transmit data is proven to be a NP-hard problem. The objective of the proposed method is to find an optimal time division multiple access (TDMA) frame, based on maximizing the channel utilization. The proposed method benefits from an on-demand noise injection policy, which injects noise based on the status of neuron and its neighborhoods. The method is superior to other Noise Chaotic Neural Networks (NCNN) that suffer from blind injection policy. The experimental result shows that, in most cases, the proposed on-demand noise injection algorithm finds the best solution with minimal average time delay and maximum channel utilization.