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
Link To Document :
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