Noisy Chaotic Neural Networks With Variable Thresholds for the Frequency Assignment Problem in Satellite Communications

We propose a novel approach, i.e., a noisy chaotic neural network with variable thresholds (NCNN-VT), to solve the frequency assignment problem in satellite communications. The objective of this NP-complete optimization problem is to minimize cochannel interference between two satellite systems by r...

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Bibliographic Details
Published in:IEEE transactions on human-machine systems 2008-03, Vol.38 (2), p.209-217
Main Authors: Lipo Wang, Lipo Wang, Wen Liu, Wen Liu, Haixiang Shi, Haixiang Shi
Format: Article
Language:English
Subjects:
Applied sciences
Chaos
Chaos theory
Chaotic communication
combinatorial optimization
Computer science
control theory
systems
Computer systems and distributed systems. User interface
Exact sciences and technology
Financial advantage program
Flows in networks. Combinatorial problems
Frequency
Frequency assignment
frequency assignment problem (FAP)
Indium tin oxide
Interchannel interference
Interference
Neural networks
Neurons
noisy chaotic neural networks (NCNN)
NP-complete
Operational research and scientific management
Operational research. Management science
Optimization
Orbits
Satellite broadcasting
Satellite communication
Satellite communications
Software
Thresholds
variable thresholds
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Summary:We propose a novel approach, i.e., a noisy chaotic neural network with variable thresholds (NCNN-VT), to solve the frequency assignment problem in satellite communications. The objective of this NP-complete optimization problem is to minimize cochannel interference between two satellite systems by rearranging frequency assignments. The NCNN-VT model consists N times M of noisy chaotic neurons for an N-carrier M-segment problem. The NCNN-VT facilitates the interference minimization by mapping the objective to variable thresholds (biases) of the neurons. The performance of the NCNN-VT is demonstrated by solving a set of benchmark problems and randomly generated test instances. The NCNN-VT achieves better solutions, i.e., smaller interference with much lower computation cost compared to existing algorithms.
ISSN:1094-6977
2168-2291
1558-2442
2168-2305
DOI:10.1109/TSMCC.2007.913915