Robust power control for underlay cognitive radio networks under probabilistic quality of service and interference constraints

In cognitive radio networks, conventional power control algorithms (PCAs) based on instantaneous perfect channel gain may lead to performance degradation in practical systems, since channel uncertainties are inevitable because of quantisation errors and estimation errors. As a result, robustness of...

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Bibliographic Details
Published in:IET communications Vol. 8; no. 18; pp. 3333 - 3340
Main Authors: Xu, Yongjun, Zhao, Xiaohui
Format: Journal Article
Language:English
Published: The Institution of Engineering and Technology 18.12.2014
Subjects:
adaptive estimation
adaptive estimation scheme
Algorithms
Channels
cognitive radio
covariance matrices
covariance matrix
deterministic distribution model
distribution‐free method
Errors
estimation error
Gaussian channels
Gaussian distribution
instantaneous perfect channel gain
Interference
interior‐point method
minimax probability machine
minimax techniques
Networks
PCA
Power control
probabilistic interference constraint
Probabilistic methods
probability
Probability theory
quality of service
quantisation error
radiofrequency interference
robust power control algorithm
second order cone programming problem
signal‐to‐interference‐and‐noise ratio constraint
telecommunication control
underlay cognitive radio network
wireless channels
ISSN:1751-8628, 1751-8636
Online Access:Get full text
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Summary:In cognitive radio networks, conventional power control algorithms (PCAs) based on instantaneous perfect channel gain may lead to performance degradation in practical systems, since channel uncertainties are inevitable because of quantisation errors and estimation errors. As a result, robustness of the algorithms becomes an important issue. However, traditional robust PCAs with probabilistic models require to perfectly know the distribution information of the estimation error (e.g. Gaussian distribution) which is difficult to obtain. Moreover, the distribution function of the actual error may not be Gaussian distribution. In this study, instead of using deterministic distribution model, a robust PCA based on a distribution‐free method is designed to minimise total transmit power of secondary users subject to probabilistic interference and signal to interference plus noise ratio constraints. Based on the minimax probability machine, the original problem is reformulated as a second order cone programming problem solved by interior‐point method. An adaptive estimation scheme is proposed to estimate the actual mean and covariance matrix of uncertain parameters. Simulation results demonstrate the effectiveness and robustness of the proposed algorithm by comparing with the robust algorithms under worst‐case constraints and probabilistic constraints, respectively.
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ISSN:1751-8628
1751-8636
DOI:10.1049/iet-com.2014.0300