Spectrum sensing based on Bayesian generalised likelihood ratio for cognitive radio systems with multiple antennas

Al-Amidie, Muthana; Al-Asadi, Ahmed; Micheas, Athanasios C.; Islam, Naz E.

doi:10.1049/iet-com.2018.5276

Cited by 7 publications

(11 citation statements)

References 20 publications

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“…Now, we assume that the noise variance has an Inverse-Gamma prior distribution with different values of shape ρ and scale parameter κ . Therefore, the expression of the prior distribution of the noise variance as shown in [32,33,49] is γ 2 ∼ Inv-Gamma (ρ, κ). In other words, a diagonal matrix Γ 0 = diag (γ 2 0,1 , .…”

Section: Methodology 41 B-glrt Detector For Unknown Noise Covariancmentioning

confidence: 99%

“…In this section, we explain our approach using synthetic data to clarify the performance of our design of the GLRT spectrum sensing algorithm where the channel uncertainty exists. In our simulation, the average received SNR at SU is modeled as shown in [33], SNR = log(tr(HH H )) − log(tr(Γ 0 )).…”

Section: Numerical Evaluationmentioning

confidence: 99%

“…HT can also be invoked in many applications such as multiple model order selections [28][29][30], nonlinear regression [31], and many others. The use of a suitable statistic test is considered a significant step in HT, i.e., the main goal in achieving an efficient detector is to increase detection probability such as [32,33] in wireless communication.…”

Section: Introductionmentioning

confidence: 99%

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Robust Spectrum Sensing Detector Based on MIMO Cognitive Radios with Non-Perfect Channel Gain

et al. 2021

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The spectrum has increasingly become occupied by various wireless technologies. For this reason, the spectrum has become a scarce resource. In prior work, the authors have addressed the spectrum sensing problem by using multi-input and multi-output (MIMO) in cognitive radio systems. We considered the detection and estimation framework for MIMO cognitive network where the noise covariance matrix is unknown with perfect channel state information. In this study, we propose a generalized likelihood ratio test (GLRT) for the spectrum sensing problem in cognitive radio where the noise covariance matrix is unknown with non-perfect channel state information. Two scenarios are examined in this study: (i) in the first scenario, the sub-optimal solution of the worst case of the system’s performance is considered; (ii) in the second scenario, we present a robust detector for the MIMO spectrum sensing problem. For both scenarios, the Bayesian approach with a generalized likelihood ratio test based on the binary hypothesis problem is used. From the results, it can be seen that our approach provides the best performance in the spectrum sensing problem under specified assumptions. The simulation results also demonstrate that our approach significantly outperforms other state-of-the-art spectrum sensing detectors when the channel uncertainty is addressed.

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Section: Methodology 41 B-glrt Detector For Unknown Noise Covariancmentioning

confidence: 99%

Section: Numerical Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robust Spectrum Sensing Detector Based on MIMO Cognitive Radios with Non-Perfect Channel Gain

et al. 2021

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“…There are several kinds of literature investigating various aspects of spectrum sensing for CR systems in recent years. In [10], a new spectrum sensing based on the Generalised Likelihood Ratio Test is proposed. In this method, a circular complex Gaussian distribution is considered to model noise vector and the optimal detector is derived for a multiple antennas CR system.…”

Section: Related Workmentioning

confidence: 99%

Cooperative Bayesian‐based detection framework for spectrum sensing in cognitive radio networks

2019

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“…The first is overlay CRN in which the PU(s) has the highest priority to utilise the spectrum and the secondary user can use the spectrum if it is not utilised by the PU. The secondary user must have the ability to detect the spectrum hole(s) (the frequencies that are not used by the PU) through an available detection techniques such as energy detector [10], match filter detector [11], likelihood ratio test detector [12], cyclostationary detector [13], Bayesian‐based GLRT detector [14] etc. The other CRN type is underlay CRN in which the PU and the secondary user share the spectrum resource simultaneously.…”

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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Spectrum sensing based on Bayesian generalised likelihood ratio for cognitive radio systems with multiple antennas

Cited by 7 publications

References 20 publications

Robust Spectrum Sensing Detector Based on MIMO Cognitive Radios with Non-Perfect Channel Gain

Robust Spectrum Sensing Detector Based on MIMO Cognitive Radios with Non-Perfect Channel Gain

Cooperative Bayesian‐based detection framework for spectrum sensing in cognitive radio networks

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

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