A Novel DAG Spectrum Sensing Algorithm with Reducing Computational Complexity

2019 ◽  
pp. 448-458
Author(s):  
Weiting Gao ◽  
Fuwei Jiang ◽  
Fei Ma ◽  
Weilun Liu
Keyword(s):  
Computational Complexity ◽  
Spectrum Sensing

scholarly journals Efficient Approximations for Optimization of N-Out-of-K Rule for Heterogeneous Cognitive Radio Networks

2021 ◽  
Vol 11 (7) ◽  
pp. 3083
Author(s):  
Youheng Tan ◽  
Xiaojun Jing
Keyword(s):  
Cognitive Radio ◽  
Computational Complexity ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Radio Networks ◽  
Closed Form Expression ◽  
Sampling Point ◽  
Mutual Cooperation ◽  
Communication Time ◽  
Sensing Time

Spectrum sensing (SS) has attracted much attention due to its important role in the improvement of spectrum efficiency. However, the limited sensing time leads to an insufficient sampling point due to the tradeoff between sensing time and communication time. Although the sensing performance of cooperative spectrum sensing (CSS) is greatly improved by mutual cooperation between cognitive nodes, it is at the expense of computational complexity. In this paper, efficient approximations of the N-out-of-K rule-based CSS scheme under heterogeneous cognitive radio networks are provided to obtain the closed-form expression of the sensing threshold at the fusion center (FC), where the false alarm probability and its corresponding detection probability are approximated by the Poisson distribution. The computational complexity required to obtain the optimal sensing threshold at the FC has greatly decreased and theoretical derivations state that the approximation error is negligible. The simulations validate the effectiveness of the proposed scheme.

Complexity Issues within Eigenvalue-Based Multi-Antenna Spectrum Sensing

2015 ◽  
pp. 603-617 ◽  
Author(s):  
Ines Elleuch ◽  
Fatma Abdelkefi ◽  
Mohamed Siala
Keyword(s):  
Computational Complexity ◽  
False Alarm ◽  
Spectrum Sensing ◽  
Complexity Analysis ◽  
Floating Point ◽  
Beam Forming ◽  
Multiple Antenna ◽  
Deep Insight ◽  
Minimum Eigenvalue ◽  
Insight Into

This chapter provides a deep insight into multiple antenna eigenvalue-based spectrum sensing algorithms from a complexity perspective. A review of eigenvalue-based spectrum-sensing algorithms is provided. The chapter presents a finite computational complexity analysis in terms of Floating Point Operations (flop) and a comparison of the Maximum-to-Minimum Eigenvalue (MME) detector and a simplified variant of the Multiple Beam forming detector as well as the Approximated MME method. Constant False Alarm Performances (CFAR) are presented to emphasize the complexity-reliability tradeoff within the spectrum-sensing problem, given the strong requirements on the sensing duration and the detection performance.

Multistage Spectrum Sensing for Cognitive Radio Using Energy and Maximum Eigenvalues Detection

2020 ◽  
Vol 11 (4) ◽  
pp. 41-59
Author(s):  
Faten Mashta ◽  
Mohieddin Wainakh ◽  
Wissam Altabban
Keyword(s):  
Cognitive Radio ◽  
Computational Complexity ◽  
Spectrum Sensing ◽  
Energy Detection ◽  
Probability Of Detection ◽  
Smoothing Factor ◽  
Third Stage ◽  
Simulation Results ◽  
Good Detection ◽  
High Computational Complexity

Spectrum sensing in cognitive radio has difficult and complex requirements such as requiring speed and sensing accuracy at very low SNRs. In this paper, the authors propose a novel fully blind sequential multistage spectrum sensing detector to overcome the limitations of single stage detector and make use of the advantages of each detector in each stage. In first stage, energy detection is used because of its simplicity. However, its performance decreases at low SNRs. In second and third stage, the maximum eigenvalues detector is adopted with different smoothing factor in each stage. Maximum eigenvalues detection technique provide good detection performance at low SNRs, but it requires a high computational complexity. In this technique, the probability of detection improves as the smoothing factor raises at the expense of increasing the computational complexity. The simulation results illustrate that the proposed detector has better sensing accuracy than the three individual detectors and a computational complexity lies in between the three individual complexities.

scholarly journals Using FFT to reduce the computational complexity of sub-Nyquist sampling based wideband spectrum sensing

2019 ◽  
Vol 1237 ◽  
pp. 022004
Author(s):  
Changjian Liu ◽  
Kai Chen ◽  
Jie Zhang ◽  
Yifan Wang ◽  
Houjun Wang
Keyword(s):  
Computational Complexity ◽  
Spectrum Sensing ◽  
Wideband Spectrum Sensing

scholarly journals Energy-Based Spectrum Sensing under Nonreconstruction Framework

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
Yulong Gao ◽  
Yanping Chen
Keyword(s):  
Computational Complexity ◽  
Spectrum Sensing ◽  
Reconstruction Algorithm ◽  
Reconstruction Error ◽  
Normalization Method ◽  
Statistical Characteristics ◽  
Practical Applications ◽  
Fast Detection ◽  
Mean Variance ◽  
High Computational Complexity

To reduce the computational complexity and rest on less prior knowledge, energy-based spectrum sensing under nonreconstruction framework is studied. Compressed measurements are adopted directly to eliminate the effect of reconstruction error and high computational complexity caused by reconstruction algorithm of compressive sensing. Firstly, we summarize the conventional energy-based spectrum sensing methods. Next, the major effort is placed on obtaining the statistical characteristics of compressed measurements and its corresponding squared form, such as mean, variance, and the probability density function. And then, energy-based spectrum sensing under nonreconstruction framework is addressed and its performance is evaluated theoretically and experimentally. Simulations for the different parameters are performed to verify the performance of the presented algorithm. The theoretical analysis and simulation results reveal that the performance drops slightly less than that of conventional energy-normalization method and reconstruction-based spectrum sensing algorithm, but its computational complexity decreases remarkably, which is the first thing one should think about for practical applications. Accordingly, the presented method is reasonable and effective for fast detection in most cognitive scenarios.

An Integrated Parallel Multistage Spectrum Sensing for Cognitive Radio

2021 ◽  
Vol 12 (2) ◽  
pp. 1-20
Author(s):  
Faten Mashta ◽  
Mohieddin Wainakh ◽  
Wissam Altabban
Keyword(s):  
Cognitive Radio ◽  
Computational Complexity ◽  
Spectrum Sensing ◽  
Energy Detection ◽  
Maximum Eigenvalue ◽  
Two Stage ◽  
Smoothing Factor ◽  
Low Snr ◽  
Simulation Results ◽  
Good Detection

Spectrum sensing for cognitive radio requires speed and good detection performance at very low SNR ratios. There is no single-stage spectrum sensing technique that is perfect enough to be implemented in practical cognitive radio. In this paper, the authors propose a new parallel fully blind multistage detector. They assume the appropriate stage based on the estimated SNR values that are achieved from the SNR estimator. Energy detection is used in first stage for its simplicity and sensing accuracy at high SNR. For low SNRs, they adopt the maximum eigenvalues detector with different smoothing factor in higher stages. The sensing accuracy for the maximum eigenvalue detector technique improves with higher value of the smoothing factor. However, the computational complexity will increase significantly. They analyze the performance of two cases of the proposed detector: two-stage and three-stage schemes. The simulation results show that the proposed detector improves spectrum sensing in terms of accuracy and speed.

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