DocumentCode
790682
Title
A channel effect prediction-based power control scheme using PRNN/ERLS for uplinks in DS-CDMA cellular mobile systems
Author
Chen, Yih-Shen ; Chang, Chung-Ju ; Hsieh, Yi-Lin
Author_Institution
Nat. Chiao Tung Univ., Hsinchu, Taiwan
Volume
5
Issue
1
fYear
2006
Firstpage
23
Lastpage
27
Abstract
This paper proposes a channel effect prediction based power control scheme using pipeline recurrent neural network (PRNN)/extended recursive least squares (ERLS) for uplinks in direct sequence code division multiple access (DS-CDMA) cellular mobile systems. Conventional signal-to-interference (SIR) prediction-based power control schemes may incur prediction mistakes caused by the adjustment of transmission power. The proposed power control scheme purely tracks the variation of channel effect and, thus, can be immune to any power adjustment. Furthermore, it adopts the PRNN with ERLS for predicting the channel effect. Simulation results show that the channel effect prediction-based power control scheme using PRNN/ERLS achieves a 40% higher system capacity and a lower outage probability than the conventional SIR prediction-based power control scheme using grey prediction method (IEEE Trans. Veh. Technol., Vol. 49, No. 6, p. 2081, 2000).
Keywords
cellular radio; code division multiple access; least squares approximations; neurocontrollers; power control; predictive control; radio links; radiofrequency interference; recurrent neural nets; spread spectrum communication; telecommunication congestion control; DS-CDMA uplinks; cellular mobile systems; channel effect prediction-based power control scheme; direct sequence code division multiple access; extended recursive least squares; grey prediction method; outage probability; pipeline recurrent neural network; power adjustment; signal-to-interference prediction; Capacity planning; Cellular networks; Direct-sequence code-division multiple access; Least squares methods; Multiaccess communication; Pipeline processing; Power control; Prediction methods; Predictive models; Recurrent neural networks;
fLanguage
English
Journal_Title
Wireless Communications, IEEE Transactions on
Publisher
ieee
ISSN
1536-1276
Type
jour
DOI
10.1109/TWC.2006.1576521
Filename
1576521
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