Optimal GLRT-Based Robust Spectrum Sensing for MIMO Cognitive Radio Networks With CSI Uncertainty

Patel, Adarsh; Biswas, Sinchan; Jagannatham, Aditya K.

doi:10.1109/tsp.2015.2500183

Cited by 20 publications

(9 citation statements)

References 29 publications

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“…Then troposcatter signal on this distortionless beam can be denoted as

X_{()(, M \times N)} = S_{()(, M \times N)}

⊙

H_{tro ()(, M \times N)} + N_{()(, M \times N)}

, where

S_{()(, M \times N)} = {[\begin{matrix} bold-italics & bold-italics & \begin{matrix} \dots & bold-italics \end{matrix} \end{matrix}]}^{T}

. Without losing generality, the target signals are assumed to be zero‐mean complex Gaussian with a variance of

σ_{s}^{2}

as in [12, 13]. To simplify the system model, the equivalent zero‐mean independent and identically distributed (i.i.d.)…”

Section: System and Channel Fading Modelsmentioning

confidence: 99%

“…However, the covariance‐based detectors only considered spatial correlation information in the sample matrix of captured array signals. Owing to its parameter estimation capability, the generalised likelihood ratio test (GLRT) [12, 13] is a promising method to deal with uncertain channel responses. For the unknown channel gain scenario, the maximum a posteriori probability estimation of channel gain was derived to develop a Bayesian GLRT for finite samples in [12], wherein the channel gain was assumed to be a circular complex Gaussian distribution and spatially correlated.…”

Section: Introductionmentioning

confidence: 99%

“…Owing to its parameter estimation capability, the generalised likelihood ratio test (GLRT) [12, 13] is a promising method to deal with uncertain channel responses. For the unknown channel gain scenario, the maximum a posteriori probability estimation of channel gain was derived to develop a Bayesian GLRT for finite samples in [12], wherein the channel gain was assumed to be a circular complex Gaussian distribution and spatially correlated. A robust GLRT was proposed to address the uncertainty in available channel state information (CSI) estimations in [13], where the channel coefficients and uncertainty were investigated spatially among antennas.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Target detection for a kind of troposcatter over‐the‐horizon passive radar based on channel fading information

Wang

Zhu

et al. 2018

IET Radar, Sonar & Navigation

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“…Then troposcatter signal on this distortionless beam can be denoted as

X_{()(, M \times N)} = S_{()(, M \times N)}

⊙

H_{tro ()(, M \times N)} + N_{()(, M \times N)}

, where

S_{()(, M \times N)} = {[\begin{matrix} bold-italics & bold-italics & \begin{matrix} \dots & bold-italics \end{matrix} \end{matrix}]}^{T}

. Without losing generality, the target signals are assumed to be zero‐mean complex Gaussian with a variance of

σ_{s}^{2}

as in [12, 13]. To simplify the system model, the equivalent zero‐mean independent and identically distributed (i.i.d.)…”

Section: System and Channel Fading Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Target detection for a kind of troposcatter over‐the‐horizon passive radar based on channel fading information

Wang

Zhu

et al. 2018

IET Radar, Sonar & Navigation

View full text Add to dashboard Cite

“…The full benefit of MIMO in CRN can be exploited when the channel state information (CSI) is perfectly gathered, thus much research has been done in this area such as [17–20]. In practice and due to the nature of wireless communication channels, the estimated CSI is subject to error, and much research attention has been given to its mitigation in wireless communication [21, 22].…”

Section: Introductionmentioning

confidence: 99%

Robust underlay cognitive network download beamforming in multiple users, multiple groups multicell scenario

et al. 2020

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Achieving high quality of service (QOS) at the end‐users while maintaining the low interference power at the primary users is the main goal in underlay cognitive radio networks. This goal becomes a more difficult task in the designing of beamforming vectors where all channels state information (CSIs) in the secondary network, as well as the interference CSIs, are uncertain. This task is addressed in this study using an iterative optimization technique. In this technique, the original CSI problem, which is difficult to solve as a single optimization problem, is instead separated into two sub‐problems. The first subproblem is the interference power problem, which can be solved either sub‐optimally or optimally using Lagrange duality. The second sub‐problem is the QOS problem, which can be solved either sub‐optimally or robustly using non‐monotone spectral projected gradient method. The two sub‐problem solutions are then recombined into a single problem to extract beamforming vectors. Two methods are invoked to extract the beamforming vectors: either the successive convex approximation (SCA) method or the bisection search method. Theoretical analysis and simulation results indicate that the two methods can offer a tradeoff between better QOS (using bisection search method) or less computational complexity (using SCA method)

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“…Much research has been conducted to obtain the CSI in MIMO systems such as [7, 8]. However, due to several practical limitations in wireless communication, such as the dynamic characteristics of channels [9], limited feedback [10, 11] and/or estimation error [12], the estimated CSI contains some error and thus the uncertainty in the observed CSI should be taken into consideration. This line of research has been addressed in [13–17] and the references therein.…”

Section: Introductionmentioning

confidence: 99%

Worst case fair beamforming for multiple multicast groups in multicell networks

et al. 2019

View full text Add to dashboard Cite

Optimal GLRT-Based Robust Spectrum Sensing for MIMO Cognitive Radio Networks With CSI Uncertainty

Cited by 20 publications

References 29 publications

Target detection for a kind of troposcatter over‐the‐horizon passive radar based on channel fading information

Target detection for a kind of troposcatter over‐the‐horizon passive radar based on channel fading information

Robust underlay cognitive network download beamforming in multiple users, multiple groups multicell scenario

Worst case fair beamforming for multiple multicast groups in multicell networks

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