A neural network approach to broadcast scheduling in multi-hop radio networks

The problem of scheduling interference-free transmissions with maximum throughput in a multi-hop radio network is NP-complete. The computational complexity becomes intractable as the network size increases. In this paper, the scheduling is formulated as a combinatorial optimization problem. An efficient neural network approach, namely, mean field annealing, is applied to obtain optimal transmission schedules. Numerical examples show that this method is capable of finding an interference-free schedule with (almost) optimal throughput.<<ETX>>

[1]  Carsten Peterson,et al.  Neural Networks and NP-complete Optimization Problems; A Performance Study on the Graph Bisection Problem , 1988, Complex Syst..

[2]  Keshab K. Parhi,et al.  Distributed scheduling of broadcasts in a radio network , 1989, IEEE INFOCOM '89, Proceedings of the Eighth Annual Joint Conference of the IEEE Computer and Communications Societies.

[3]  Anthony Ephremides,et al.  Scheduling broadcasts in multihop radio networks , 1990, IEEE Trans. Commun..

[4]  F. Kamoun,et al.  A neural network shortest path algorithm for optimum routing in packet-switched communications networks , 1991, IEEE Global Telecommunications Conference GLOBECOM '91: Countdown to the New Millennium. Conference Record.

[5]  Carsten Peterson,et al.  Explorations of the mean field theory learning algorithm , 1989, Neural Networks.

[6]  Imrich Chlamtac,et al.  On Broadcasting in Radio Networks - Problem Analysis and Protocol Design , 1985, IEEE Transactions on Communications.

[7]  K.-W. Hung,et al.  Fair and efficient transmission scheduling in multihop packet radio networks , 1992, [Conference Record] GLOBECOM '92 - Communications for Global Users: IEEE.