DocumentCode :
3357112
Title :
Comparison of two optimization techniques for channel assignment in cellular radio network
Author :
Kim, Jae-Soo ; Park, Sahng H. ; Dowd, Patrick W. ; Nasrabadi, Nasser M.
Author_Institution :
Dept. of Electr. & Comput. Eng., State Univ. of New York, Buffalo, NY, USA
Volume :
3
fYear :
1995
fDate :
18-22 Jun 1995
Firstpage :
1850
Abstract :
The channel assignment problem has become increasingly important in mobile telephone communication. Since the usable range of the frequency spectrum is limited, the optimal assignment problem of channels has become increasingly important. Two optimization methods, the neural networks and the genetic algorithms are applied to the channel assignment problem. To avoid falling into the local minima in the neural networks, certain techniques such as forced assignment and changing the cell list order, are used. In the genetic algorithms approach, the proper genetic operators are developed. All three constraints are also considered for the channel assignments: the co-channel constraint, the adjacent channel constraint and the co-site channel constraint. As simulation results, the average iteration (or generation) numbers, the convergence rates and the CPU times according to the various techniques for the two approaches are presented and compared
Keywords :
Hopfield neural nets; cellular radio; frequency allocation; genetic algorithms; land mobile radio; radio networks; radio spectrum management; radiotelephony; CPU times; adjacent channel constraint; average generation numbers; average iteration numbers; cell list order; cellular radio network; channel assignment; cochannel constraint; convergence rates; cosite channel constraint; forced assignment; frequency spectrum; genetic algorithms; genetic operators; mobile telephone communication; neural networks; optimization techniques; simulation results; Biological neural networks; Cellular neural networks; Frequency; Genetic algorithms; Intelligent networks; Land mobile radio cellular systems; Mobile computing; Neural networks; Neurons; Optimization methods;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Communications, 1995. ICC '95 Seattle, 'Gateway to Globalization', 1995 IEEE International Conference on
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-2486-2
Type :
conf
DOI :
10.1109/ICC.1995.524518
Filename :
524518
Link To Document :
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