An Improved Neural Network for Channel Assignment Problems in Cellular Mobile Communication Systems
Summary :
This paper presents an improved neural network for channel assignment problems in cellular mobile communication systems in the new co-channel interference model. Sengoku et al. first proposed the neural network for the same problem, which can find solutions only in small size cellular systems with up to 40 cells in our simulations. For the practical use in the next generation's cellular systems, the performance of our improved neural network is verified by large size cellular systems with up to 500 cells. The newly defined energy function and the motion equation with two heuristics in our neural network achieve the goal of finding optimum or near-optimum solutions in a nearly constant time.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E78-B No.8 pp.1187-1196
- Publication Date
- 1995/08/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Special Section PAPER (Special Issue on Technologies for High-Speed Mobile Communications)
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Nobuo FUNABIKI, Seishi NISHIKAWA, "An Improved Neural Network for Channel Assignment Problems in Cellular Mobile Communication Systems" in IEICE TRANSACTIONS on Communications,
vol. E78-B, no. 8, pp. 1187-1196, August 1995, doi: .
Abstract: This paper presents an improved neural network for channel assignment problems in cellular mobile communication systems in the new co-channel interference model. Sengoku et al. first proposed the neural network for the same problem, which can find solutions only in small size cellular systems with up to 40 cells in our simulations. For the practical use in the next generation's cellular systems, the performance of our improved neural network is verified by large size cellular systems with up to 500 cells. The newly defined energy function and the motion equation with two heuristics in our neural network achieve the goal of finding optimum or near-optimum solutions in a nearly constant time.
URL: https://global.ieice.org/en_transactions/communications/10.1587/e78-b_8_1187/_p
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@ARTICLE{e78-b_8_1187,
author={Nobuo FUNABIKI, Seishi NISHIKAWA, },
journal={IEICE TRANSACTIONS on Communications},
title={An Improved Neural Network for Channel Assignment Problems in Cellular Mobile Communication Systems},
year={1995},
volume={E78-B},
number={8},
pages={1187-1196},
abstract={This paper presents an improved neural network for channel assignment problems in cellular mobile communication systems in the new co-channel interference model. Sengoku et al. first proposed the neural network for the same problem, which can find solutions only in small size cellular systems with up to 40 cells in our simulations. For the practical use in the next generation's cellular systems, the performance of our improved neural network is verified by large size cellular systems with up to 500 cells. The newly defined energy function and the motion equation with two heuristics in our neural network achieve the goal of finding optimum or near-optimum solutions in a nearly constant time.},
keywords={},
doi={},
ISSN={},
month={August},}
Copy
TY - JOUR
TI - An Improved Neural Network for Channel Assignment Problems in Cellular Mobile Communication Systems
T2 - IEICE TRANSACTIONS on Communications
SP - 1187
EP - 1196
AU - Nobuo FUNABIKI
AU - Seishi NISHIKAWA
PY - 1995
DO -
JO - IEICE TRANSACTIONS on Communications
SN -
VL - E78-B
IS - 8
JA - IEICE TRANSACTIONS on Communications
Y1 - August 1995
AB - This paper presents an improved neural network for channel assignment problems in cellular mobile communication systems in the new co-channel interference model. Sengoku et al. first proposed the neural network for the same problem, which can find solutions only in small size cellular systems with up to 40 cells in our simulations. For the practical use in the next generation's cellular systems, the performance of our improved neural network is verified by large size cellular systems with up to 500 cells. The newly defined energy function and the motion equation with two heuristics in our neural network achieve the goal of finding optimum or near-optimum solutions in a nearly constant time.
ER -
English
