GLRT Approach for Multi-Antenna-Based Spectrum Sensing Under Interference

Lin, Menghan; Wang, Wenjie; Hong, Xi; Zhang, Weile

doi:10.1109/lcomm.2020.2985031

Cited by 14 publications

(3 citation statements)

References 16 publications

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“…The SαS noise have infinite variance. Therefore, based on the dispersion coefficient γ of the alpha-stable distribution and the variance σ 2 s of the signal, the generalized signal-to-noise ratio (GSNR) is used as [20]…”

Section: System Model a System Modelmentioning

confidence: 99%

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Multiantenna Spectrum Sensing With Alpha-Stable Noise for Cognitive Radio-Enabled IoT

Zhang,

Liu,

Chen

et al. 2024

IEEE Internet Things J.

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Cognitive radio-enabled Internet of Things (CR-IoT) is considered as a promising technology to handle spectrum scarcity for IoT applications. Spectrum sensing enables unlicensed secondary users to exploit spectrum holes under the condition of avoiding interference with primary users in CR-IoT networks. Previous studies often assume that the noise is Gaussian while ignoring the influence of non-Gaussian noise. Moreover, multi-antenna-based spectrum sensing algorithms only consider the partial information of covariance matrix. This paper develops two multi-antenna-based spectrum sensing schemes, using fractional low-order covariance matrices to address the issue of performance degradation in impulsive noise. Specifically, the first scheme, namely, diagonal element weighting detection, exploits the diagonal element weighting of the fractional loworder covariance matrix. The latter scheme is called off-diagonal element weighting detection, which adopts the diagonal matrix weighting strategy that exploits the off-diagonal elements of fractional low-order covariance matrices. The approximate analytical expressions of the false alarm probability and detection probability are derived. These developed schemes do not employ any priori knowledge of the primary user signal. Simulation results indicate that two proposed schemes achieve acceptable performance and are robust to the characteristic exponent of the alpha-stable noise, e.g., these proposed methods could achieve a detection probability of 90% with a false alarm probability of 0.1 at GSNR = -16dB, respectively.

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Section: System Model a System Modelmentioning

confidence: 99%

“…The above two methods overcome the noise uncertainty at the cost of high computational complexity. Reference [20] developed two schemes based on the generalized likelihood ratio test under interference. These two schemes provide good performance using the knowledge of interference's statistical characteristics, but again with high computational complexity.…”

mentioning

confidence: 99%

Multiantenna Spectrum Sensing With Alpha-Stable Noise for Cognitive Radio-Enabled IoT

Zhang,

Liu,

Chen

et al. 2024

IEEE Internet Things J.

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“…IQ imbalance [47] has been considered for the proposal of novel detectors in [46], [48], while the improperty of the received complex-value signals due to the IQ imbalance has also been addressed in [49], where a constant false alarm rate detection is reported. The multi-antenna signal detection has also been studied under PU-signal correlation due to oversampling [44], and novel detectors under interference are also conceived in [50].…”

Section: Eigenvalue-based Techniquesmentioning

confidence: 99%

Spectrum Sensing Using Software Defined Radio for Cognitive Radio Networks: A Survey

Manco

Dayoub

Nafkha³

et al. 2022

IEEE Access

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Cognitive radio (CR) network has emerged as a potential solution to the under-utilization problem of the allocated radio spectrum, where spectrum sensing (SS) plays a key role to enable the coexistence between primary and secondary users. It has attracted research interests, and several works have been reported in the literature. Nevertheless, the assumptions and simplifications introduced during the modeling of the communication system often yield misleading conclusions each time relevant aspects of their implementation on a testbed are omitted. Hence, prototypes are built to study their behaviour under real-world conditions, therefore software defined radio (SDR) has emerged as an ideal vehicle to allow researchers to experiment with prototypes of these CR approaches. In this survey, we provide an overview of the latest works in CR networks related to the spectrum awareness approaches and taking into account their implementation on testbeds. These approaches are classified from a practical point of view, where a detailed review of the existing works for each category is provided. A review of the existing SDR platforms is also exposed highlighting the main components and features of current architectures employed for experimental evaluation of CR approaches. Next, the challenges to implement current spectrum awareness approaches on SDR platforms are detailed. Finally, at the light of these reviews, research challenges and open issues are identified for future research directions. INDEX TERMSCognitive radio, spectrum sensing, SDR platforms, hardware impairments.

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Gerschgorin Radii and Centers Ratio Detector for Multi-antenna Spectrum Sensing in Cognitive Networks

Bharathy¹,

Rajendran

2022

Advances in Intelligent Systems and Computing

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GLRT Approach for Multi-Antenna-Based Spectrum Sensing Under Interference

Cited by 14 publications

References 16 publications

Multiantenna Spectrum Sensing With Alpha-Stable Noise for Cognitive Radio-Enabled IoT

Multiantenna Spectrum Sensing With Alpha-Stable Noise for Cognitive Radio-Enabled IoT

Spectrum Sensing Using Software Defined Radio for Cognitive Radio Networks: A Survey

Gerschgorin Radii and Centers Ratio Detector for Multi-antenna Spectrum Sensing in Cognitive Networks

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