NDLI: Fuzzy/neural congestion control for integrated voice and data DS-CDMA/FRMA cellular networks

Content Provider	IEEE Xplore Digital Library
Author	Chung-Ju Chang Bo-Wei Chen Terng-Yuan Liu Fang-Ching Ren
Copyright Year	1983
Abstract	The paper proposes congestion control using fuzzy/neural techniques for integrated voice and data direct-sequence code division multiple access/frame reservation multiple access (DS-CDMA/FRMA) cellular networks. The fuzzy/neural congestion controller is constituted by a pipeline recurrent neural network (PRNN) interference predictor, a fuzzy performance indicator, and a fuzzy/neural access probability controller. It regulates the traffic input to the integrated voice and data DS-CDMA/FRMA cellular system by determining proper access probabilities for users so that congestion can be avoided and the throughput can be maximized. Simulation results show that the DS-CDMA/FRMA fuzzy/neural congestion controllers perform better than conventional DS-CDMA/PRMA with channel access function in terms of voice packet dropping ratio, corruption ratio, and utilization. In addition, the neural congestion controller outperforms the fuzzy congestion controller.
Starting Page	283
Ending Page	293
Page Count	11
File Size	298478
File Format	PDF
ISSN	07338716
Volume Number	18
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2000-02-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy control Multiaccess communication Fuzzy neural networks Recurrent neural networks Land mobile radio cellular systems Pipeline processing Interference Communication system traffic control Traffic control Throughput
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Electrical and Electronic Engineering

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