Beamformings for spectrum sharing in cognitive radio networks

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Publication Details

Author listSuleesathira R., Puranachikeeree S.

PublisherWorld Scientific and Engineering Academy and Society (WSEAS)

Publication year2013

JournalWSEAS Transactions on Signal Processing (1790-5052)

Volume number9

Issue number2

Start page41

End page50

Number of pages10

ISSN1790-5052

URLhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84885824116&partnerID=40&md5=e2f158225215c61355215bad875e00f4

LanguagesEnglish-Great Britain (EN-GB)


Abstract

Cognitive radio is regarded as one the most promising technology for supporting spectrum sharing which secondary (cognitive) users coexist with users in primary network whose radio band is licensed. Two conflicting challenges are how to maintain the interferences generated by the cognitive radio network to the primary network below an acceptable threshold level while maximizing the sum-rate of the cognitive radio network. We present two beamforming methods, modified zero forcing beamforming and transmit-receive beamforming. The zero forcing beamforming is modified by adding the channel gain between the cognitive radio base station and the primary user to meet the two conflicting goals. The orthogonality of transmit beams in MIMO beamforming by Gram-Schmidt method achieves the first goal that the primary user is interference free. To satisfy the second goal, self-interference is reduced by the constrained minimization of the mean output array of cognitive receivers. To reduce complexity of the system, the number of cognitive radio users must be limited. Criteria to select the number of best cognitive radio users should increase the sum-rate of cognitiveradio network. Subspace-based scheduling scheme selects the cognitive radio users orthogonal each other as much as possible so that the self-interference is mitigated. Simulation results are given to evaluate the performances of the proposed methods in forms of bit error rates, symbol error rates and sum-rates.


Keywords

BeamformingCognitive radiosConstrained optimization


Last updated on 2022-06-01 at 15:56