1. A gradual noisy chaotic neural network for solving the broadcast scheduling problem in packet radio networks
- Author
-
Lipo Wang and Haixiang Shi
- Subjects
Theoretical computer science ,Artificial neural network ,Computational complexity theory ,Computer Networks and Communications ,Computer science ,business.industry ,Time division multiple access ,General Medicine ,Radio ,Computer Science Applications ,Scheduling (computing) ,Intelligent Network ,Packet switching ,Electricity ,Nonlinear Dynamics ,Artificial Intelligence ,Packet radio networks ,Neural Networks, Computer ,business ,Radio broadcasting ,Algorithm ,Software ,Communication channel - Abstract
In this paper, we propose a gradual noisy chaotic neural network (G-NCNN) to solve the NP-complete broadcast scheduling problem (BSP) in packet radio networks. The objective of the BSP is to design an optimal time-division multiple-access (TDMA) frame structure with minimal TDMA frame length and maximal channel utilization. A two-phase optimization is adopted to achieve the two objectives with two different energy functions, so that the G-NCNN not only finds the minimum TDMA frame length but also maximizes the total node transmissions. In the first phase, we propose a G-NCNN which combines the noisy chaotic neural network (NCNN) and the gradual expansion scheme to find a minimal TDMA frame length. In the second phase, the NCNN is used to find maximal node transmissions in the TDMA frame obtained in the first phase. The performance is evaluated through several benchmark examples and 600 randomly generated instances. The results show that the G-NCNN outperforms previous approaches, such as mean field annealing, a hybrid Hopfield network-genetic algorithm, the sequential vertex coloring algorithm, and the gradual neural network.
- Published
- 2006