2015 International Conference on Computing, Networking and Communications (ICNC) 2015
DOI: 10.1109/iccnc.2015.7069384
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On effects of imperfect channel state information on null space based cognitive MIMO communication

Abstract: Abstract-In cognitive radio networks, when secondary users transmit in the null space of their interference channel with primary user, they can avoid interference. However, performance of this scheme depends on knowledge of channel state information for secondary user to perform inverse waterfilling. We evaluate the effects of imperfect channel estimation on error rates and performance degradation of primary user and elucidate the tradeoffs, such as amount of interference and guard distance. Results show that,… Show more

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Cited by 4 publications
(5 citation statements)
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“…However, shadowing and multipath fading may cause that the autocorrelation matrices in (11) and (12) differ in rank, leading to two possible situations…”
Section: Minimum-norm Waveform Optimizationmentioning
confidence: 99%
See 1 more Smart Citation
“…However, shadowing and multipath fading may cause that the autocorrelation matrices in (11) and (12) differ in rank, leading to two possible situations…”
Section: Minimum-norm Waveform Optimizationmentioning
confidence: 99%
“…Traditional null space techniques propose transmitting over the noise subspace taking advantage of the second-order statistics of the aforementioned channel. Even though these techniques overcome the implementation issues by using the noise eigenvectors [10], [11], there is still a persisting ambiguity among the adopted noise eigenvectors due to the lack of rotational invariance, leading to non-coherent detection.…”
Section: Introductionmentioning
confidence: 99%
“…For instance, noise subspace-based waveforms are used to precode opportunistic signals. The main dissemblance between different strategies is that side information may be acquired statistically [5], [6], imperfectly [7][8][9] or instantaneously [10][11][12]. References therein are suggested to the reader for further information.…”
Section: Introductionmentioning
confidence: 99%
“…, g ] . To calculate M by singular value decomposition (SVD) [26], the projection matrix is derived from the following equations:…”
Section: Projection Matrixmentioning
confidence: 99%