Robust beamforming in cognitive radio
This letter considers the multi-antenna cognitive radio (CR) network, which has a single secondary user (SU) and coexists with a primary network of multiple users. Our objective is to maximize the service probability of the SU, subject to the interference constraints on the primary users (PUs) in th...
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Published in | IEEE transactions on wireless communications Vol. 9; no. 2; pp. 570 - 576 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
New York, NY
IEEE
01.02.2010
Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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Abstract | This letter considers the multi-antenna cognitive radio (CR) network, which has a single secondary user (SU) and coexists with a primary network of multiple users. Our objective is to maximize the service probability of the SU, subject to the interference constraints on the primary users (PUs) in the form of probability. Exploiting imperfect channel state information (CSI), with its error modeled by added Gaussian noise, we address the optimization for the beamforming weights at the secondary transmitter. In particular, this letter devises an iterative algorithm that can efficiently obtain the robust optimal beamforming solution. For the case with one PU, we show that a much simpler algorithm based on a closed-form solution for the antenna weights of a given power can be presented. Numerical results reveal that the optimal solution for the constructed problem provides an effective means to tradeoff the performance between the PUs and the SU, bridging the non-robust and worst-case based systems. |
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AbstractList | This letter considers the multi-antenna cognitive radio (CR) network, which has a single secondary user (SU) and coexists with a primary network of multiple users. Our objective is to maximize the service probability of the SU, subject to the interference constraints on the primary users (PUs) in the form of probability. Exploiting imperfect channel state information (CSI), with its error modeled by added Gaussian noise, we address the optimization for the beamforming weights at the secondary transmitter. In particular, this letter devises an iterative algorithm that can efficiently obtain the robust optimal beamforming solution. For the case with one PU, we show that a much simpler algorithm based on a closed-form solution for the antenna weights of a given power can be presented. Numerical results reveal that the optimal solution for the constructed problem provides an effective means to tradeoff the performance between the PUs and the SU, bridging the non-robust and worstcase based systems. This letter considers the multi-antenna cognitive radio (CR) network, which has a single secondary user (SU) and coexists with a primary network of multiple users. Our objective is to maximize the service probability of the SU, subject to the interference constraints on the primary users (PUs) in the form of probability. Exploiting imperfect channel state information (CSI), with its error modeled by added Gaussian noise, we address the optimization for the beamforming weights at the secondary transmitter. In particular, this letter devises an iterative algorithm that can efficiently obtain the robust optimal beamforming solution. For the case with one PU, we show that a much simpler algorithm based on a closed-form solution for the antenna weights of a given power can be presented. Numerical results reveal that the optimal solution for the constructed problem provides an effective means to tradeoff the performance between the PUs and the SU, bridging the non-robust and worst-case based systems. |
Author | Kai-kit Wong Tung-Sang Ng Shaodan Ma Gan Zheng |
Author_xml | – sequence: 1 givenname: Gan surname: Zheng fullname: Zheng, Gan – sequence: 2 givenname: Shaodan surname: Ma fullname: Ma, Shaodan – sequence: 3 givenname: Kai-kit surname: Wong fullname: Wong, Kai-kit – sequence: 4 givenname: Tung-Sang surname: Ng fullname: Ng, Tung-Sang |
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Keywords | Performance evaluation Parameter estimation robust beamforming Worst case method Transmitter Iterative method Cognitive radio Algorithm Beam forming Optimization interference control Gaussian noise Channel estimation Optimal solution Numerical simulation Radio communication Antenna Software radio |
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SubjectTerms | Algorithms Antennas Applied sciences Array signal processing Beamforming Channel state information Channels Chromium Closed-form solution Cognitive radio Detection, estimation, filtering, equalization, prediction Exact sciences and technology Exact solutions Gaussian noise Information, signal and communications theory Interference constraints interference control Iterative algorithms Mathematical models Networks Noise robustness Optimization Radio Radio transmitters Radiocommunication specific techniques Radiocommunications robust beamforming Signal and communications theory Signal, noise Studies Systems, networks and services of telecommunications Telecommunications Telecommunications and information theory Transmission and modulation (techniques and equipments) |
Title | Robust beamforming in cognitive radio |
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