LRT Detectors for Spectrum Sensing of Weak OFDM Signals With Periodic Pilots

Yin, Wenshan; Chen, Hao; Xu, Datong; Yang, Yang

doi:10.1109/twc.2020.3012290

Cited by 8 publications

(4 citation statements)

References 39 publications

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“…This method is extended for SU equipped with an MA receiver in [112]. LRT-based MFDs, which utilize the OFDM signal structure for the cases when time synchronization is either available or not, are proposed in [113]. A pilot sequence s r,o (n) = s u (n) + s v (n) is defined where s u (n) appears in every OFDM symbol and s v (n) -only once every κ v symbols.…”

Section: Matched Filter-based Detectionmentioning

confidence: 99%

“…In addition, modern spectrum sensing methods are required to detect the PU in complex non-Gaussian as well as fading channels, which may degrade their performance advantage in comparison to the other detector types. Definite strengths of the MFDs are: 1) Their resilience to noise variance uncertainty [109], [113]; 2) Efficiency in very low SNR achieved through much smaller N RX and N in the MA receiver case [112], [112]; 3) Discrimination between the PU's transmission power levels (this necessitates the availability of a priori known probability for each level, which may not be trivial to obtain realistically) [110]- [112]. The prominence of OFDM-based communication systems makes training MFDs to recognize reference symbols of this kind, to become an important design consideration for spectrum sensing.…”

Section: ) Lessons Learnedmentioning

confidence: 99%

“…The prominence of OFDM-based communication systems makes training MFDs to recognize reference symbols of this kind, to become an important design consideration for spectrum sensing. If there are no other PU signal types in the region of a CR's operation, it will be beneficial to utilize this class of spectrum sensing to achieve agility and low computational complexity [113]. Additionally, MFDs can be coupled with supervised learning-based modulation classification to achieve not just the detection of the PU signal, but also its source and modulation type.…”

Section: ) Lessons Learnedmentioning

confidence: 99%

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Probabilistic Spectrum Sensing Based on Feature Detection for 6G Cognitive Radio: A Survey

et al. 2021

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With the advent of Sixth Generation (6G) telecommunication systems already envisioned, increased effort is made to further develop current communication technologies, so they can be incorporated together with the novel ones, to deliver uninterrupted and satisfactory service for any application in every location on the ground, underwater, in the air, or in space. One such technology is Cognitive Radio (CR) which has received much attention due to its potential for increase of utilization, especially in the bands below 6 GHz. The main enabler for CR is spectrum sensing because it provides the opportunity for dynamic assessment of the radio environment to identify unused channels. This functionality has been the object of many research works for that very reason. In spite of this, the provision of accurate and fast spectrum characterization in time, frequency and space has proven to be a non-trivial task. This paper presents a detailed review of probabilistic spectrum sensing methods classified by the feature they extract from the received signal samples, to provide accurate detection of the primary user (PU) signal. The main design characteristics (such as probability of detection, robustness to noise and fading, signal/noise model assumptions, and computational complexity), strengths and weaknesses for each type are also summarized. Based on current concepts for 6G networks and applications, a framework for human-centric cognition-based wireless access is presented, which specifies the role of spectrum sensing-based CR in future networks.INDEX TERMS 6G, cognitive radio, feature detection, human-centric wireless access, Internet of Things, probabilistic spectrum sensing, wireless communications.

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Section: Matched Filter-based Detectionmentioning

confidence: 99%

Section: ) Lessons Learnedmentioning

confidence: 99%

Section: ) Lessons Learnedmentioning

confidence: 99%

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Probabilistic Spectrum Sensing Based on Feature Detection for 6G Cognitive Radio: A Survey

et al. 2021

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“…The likelihood ratio test (LRT) based on the Neyman-Pearson criterion is currently known as the optimal detection method, which requests a known distribution of the received signal that is generally related to the PU information, the noise variance, and channel statistic characteristics. Therefore, the LRT is an un-blind detection method [8]. Conversely, when the detection method relies only on the obtained signal samples without any prior information, it is referred to as a fully blind detection method.…”

Section: Introductionmentioning

confidence: 99%

Lifting Wavelet-Assisted EM Joint Estimation and Detection in Cooperative Spectrum Sensing

Tian,

Zhao,

Chen

et al. 2023

Sensors

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Spectrum sensing in Cognitive radio (CR) is a way to improve spectrum utilization by detecting spectral holes to achieve a dynamic allocation of spectrum resources. As it is often difficult to obtain accurate wireless environment information in real-world scenarios, the detection performance is limited. Signal-to-noise ratio (SNR), noise variance, and channel prior occupancy rate are critical parameters in wireless spectrum sensing. However, obtaining these parameter values in advance is challenging in practical scenarios. A lifting wavelet-assisted Expectation-Maximization (EM) joint estimation and detection method is proposed to estimate multiple parameters and achieve full-blind detection, which uses lifting wavelet in noise variance estimation to improve detection probability and convergence speed. Moreover, a stream learning strategy is used in estimating SNR and channel prior occupancy rate to fit the scenario where the SU has mobility. The simulation results demonstrate that the proposed method can achieve comparable detection performance to the semi-blind EM method.

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Co-variance Based Adaptive Threshold Spectrum Detection Optimized with Chameleon Swarm Optimization for Optimum Threshold Selection in Cognitive Radio Networks

Charan

Pandey

2023

Wireless Pers Commun

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LRT Detectors for Spectrum Sensing of Weak OFDM Signals With Periodic Pilots

Cited by 8 publications

References 39 publications

Probabilistic Spectrum Sensing Based on Feature Detection for 6G Cognitive Radio: A Survey

Probabilistic Spectrum Sensing Based on Feature Detection for 6G Cognitive Radio: A Survey

Lifting Wavelet-Assisted EM Joint Estimation and Detection in Cooperative Spectrum Sensing

Co-variance Based Adaptive Threshold Spectrum Detection Optimized with Chameleon Swarm Optimization for Optimum Threshold Selection in Cognitive Radio Networks

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