Adaptive antenna selection by parallel QR-factorization for cognitive radio cloud network

As the powerful cloud-computing infrastructures become more and more popular, the potential of their applications for dealing with the challenges emerging in cognitive radio networks (CRNs) is under scientific investigation. By making use of the parallel computing capacity of the cloud, we propose i...

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Published in:Globecom (2003) pp. 882 - 887
Main Authors: Shih Yu Chang, Hsiao-Chun Wu
Format: Conference Proceeding
Language:English
Published: IEEE 01.12.2014
Subjects:
Adaptive arrays
Antenna subset selection
Bit error rate
cloud computing
Cognitive radio
MIMO
parallel QR-factorization algorithms
Radio transmitters
Transmitting antennas
Vectors
ISSN:1930-529X
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Abstract As the powerful cloud-computing infrastructures become more and more popular, the potential of their applications for dealing with the challenges emerging in cognitive radio networks (CRNs) is under scientific investigation. By making use of the parallel computing capacity of the cloud, we propose innovative parallel QR-factorization algorithms to establish an adaptive transmitter system by dynamically selecting the antennae. Our proposed parallel algorithms can efficiently calculate a tight (achievable) lower-bound of the free distance, which determines the error probability of the symbol detection at the receiver. In this paper, we devise a new parallel QR-based antenna selection scheme in the transmitter to maximize the above-stated lower-bound for achieving the nearly optimal symbol detection at the receiver. Monte Carlo simulation results demonstrate that our proposed parallel method leads to a better bit-error-rate (BER) performance than the conventional singular-value-decomposition (SVD) based scheme. The time complexity analysis is also presented for our proposed parallel algorithms.
AbstractList As the powerful cloud-computing infrastructures become more and more popular, the potential of their applications for dealing with the challenges emerging in cognitive radio networks (CRNs) is under scientific investigation. By making use of the parallel computing capacity of the cloud, we propose innovative parallel QR-factorization algorithms to establish an adaptive transmitter system by dynamically selecting the antennae. Our proposed parallel algorithms can efficiently calculate a tight (achievable) lower-bound of the free distance, which determines the error probability of the symbol detection at the receiver. In this paper, we devise a new parallel QR-based antenna selection scheme in the transmitter to maximize the above-stated lower-bound for achieving the nearly optimal symbol detection at the receiver. Monte Carlo simulation results demonstrate that our proposed parallel method leads to a better bit-error-rate (BER) performance than the conventional singular-value-decomposition (SVD) based scheme. The time complexity analysis is also presented for our proposed parallel algorithms.
Author Shih Yu Chang
Hsiao-Chun Wu
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  surname: Shih Yu Chang
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  surname: Hsiao-Chun Wu
  fullname: Hsiao-Chun Wu
  email: wu@ece.lsu.edu
  organization: Sch. of Electr. Eng. & Comput. Sci., Louisiana State Univ., Baton Rouge, LA, USA
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StartPage 882
SubjectTerms Adaptive arrays
Antenna subset selection
Bit error rate
cloud computing
Cognitive radio
MIMO
parallel QR-factorization algorithms
Radio transmitters
Transmitting antennas
Vectors
Title Adaptive antenna selection by parallel QR-factorization for cognitive radio cloud network
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