A Metaheuristic based approach for Threshold Optimization for Spectrum Sensing in Cognitive Radio Networks

2019 ◽  
Vol 13 ◽  
Author(s):  
Garima Mahendru ◽  
Anil K Shukla ◽  
L M Patnaik
Keyword(s):  
Particle Swarm Optimization ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Detection Probability ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Energy Detection ◽  
Swarm Optimization ◽  
Noise Uncertainty ◽  
Low Snr

Background: : The mounting growth of wireless technology is attracting high demand for frequency spectrum. The measurements of spectrum usage depicts that a significant portion of spectrum lays unoccupied or overcrowded. The main cause of the glitch is the existing inefficient and fixed scheme of spectral allocation. Cognitive radio is one such technology that permits wireless devices to detect the unused frequency band and reconfigure its operating parameters to attain required quality of service. Objective: To permit dynamic allocation of the frequency band, spectrum sensing is performed which is an essential function of Cognitive radio and involves detection of an unused spectrum space to setup a communication link. Method: : This paper presents a meta-heuristic approach for selection of a decision threshold for energy detection based spectrum sensing. At low SNR and in presence of noise uncertainty performance of energy detection method fails. A novel adaptive double threshold based spectrum-sensing method is proposed to avoid such a sensing failure. Further, the metaheuristic approach employs Particle Swarm Optimization (PSO) algorithm to compute an optimal value of the threshold to attain robustness against noise uncertainty at low SNR. Results: : The simulation results of the proposed metaheuristic double threshold based spectrum sensing method demonstrate enhanced performance in comparison to the existing methods in terms of reduced error rate and increased detection probability. Some of the existing methods have been analyzed and compared from a survey of recent patents on spectrum sensing methods to support the new findings The concept of adaptive thresholding improves the detection probability by 39 % and 27 % at noise uncertainty of 1.02 and 1.04 respectively at a signal to noise ratio of -10 dB. Furthermore, the error probability reduces to 58% at the optimal threshold using Particle Swarm Optimization (PSO) algorithm for signal to noise ratio of -9 dB. Conclusions: : The main outcome of this work is reduction in probability of sensing failure and improvement in the detection probability using adaptive double thresholds at low SNR. Further, particle swarm optimization helps in obtaining minimum probability of error under noise uncertainty with an optimal threshold.

scholarly journals Crowdsensing Task Assignment Based on Particle Swarm Optimization in Cognitive Radio Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Linbo Zhai ◽  
Hua Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Large Scale ◽  
Optimal Algorithm ◽  
Particle Swarm ◽  
Task Assignment ◽  
Mobile Terminals ◽  
Swarm Optimization ◽  
Wireless Spectrum

Cognitive radio technology allows unlicensed users to utilize licensed wireless spectrum if the wireless spectrum is unused by licensed users. Therefore, spectrum sensing should be carried out before unlicensed users access the wireless spectrum. Since mobile terminals such as smartphones are more and more intelligent, they can sense the wireless spectrum. The method that spectrum sensing task is assigned to mobile intelligent terminals is called crowdsourcing. For a large-scale region, we propose the crowdsourcing paradigm to assign mobile users the spectrum sensing task. The sensing task assignment is influenced by some factors including remaining energy, locations, and costs of mobile terminals. Considering these constraints, we design a precise sensing effect function with a local constraint and aim to maximize this sensing effect to address crowdsensing task assignment. The problem of crowdsensing task assignment is difficult to solve since we prove that it is NP-hard. We design an optimal algorithm based on particle swarm optimization to solve this problem. Simulation results show our algorithm achieves higher performance than the other algorithms.

scholarly journals Performance Analysis of Spectrum Sensing in Cognitive Radio under Low SNR and Noise Floor

2019 ◽  
Vol 9 (2) ◽  
pp. 2655-2661
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Kernel Function ◽  
Feature Space ◽  
Energy Detection ◽  
Spectrum Efficiency ◽  
Dynamic Threshold ◽  
Noise Uncertainty ◽  
Low Snr ◽  
Space Data

Cognitive radio (CR) is a new technology that is proposed to improve spectrum efficiency by allowing unlicensed secondary users to access the licensed frequency bands without interfering with the licensed primary users. As there are several methods available for spectrum sensing, the energy detection (ED) is more popular due to its simple implementation. However, ED is more vulnerable to the noise uncertainty so for that reason, we present a robust detector using signal to noise ratio (SNR) with dynamic threshold energy detection technique is combined with the kernel principal component analysis (KPCA) in Cognitive Radio Networks (CRN). The primary purpose of kernel function is to ensure that its dependency relies on inner-product of data without the feature space data requirement. In this paper, with the aid of kernel function the spectrum sensing with the leading eigenvector approach is modified to a feature space of higher dimensionality.By introducing of efficient detection system with dynamic threshold facility helps the better detection levels even low SNR values with quite a lot of noise uncertainty levels. The simulation results of the proposed system reveal that KPCA outperforms with that of traditional PCA in terms of false alarm rate, detector performance when tested under various uncertainties for orthogonal frequency division multiplexing signal.

scholarly journals Virtual Angle Boundary-Aware Particle Swarm Optimization to Maximize the Coverage of Directional Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2868
Author(s):  
Gong Cheng ◽  
Huangfu Wei
Keyword(s):  
Particle Swarm Optimization ◽  
Sensor Networks ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
The Internet ◽  
Directional Sensor Networks ◽  
Swarm Optimization ◽  
Coverage Optimization ◽  
Boundary Constraint ◽  
Directional Sensor

With the transition of the mobile communication networks, the network goal of the Internet of everything further promotes the development of the Internet of Things (IoT) and Wireless Sensor Networks (WSNs). Since the directional sensor has the performance advantage of long-term regional monitoring, how to realize coverage optimization of Directional Sensor Networks (DSNs) becomes more important. The coverage optimization of DSNs is usually solved for one of the variables such as sensor azimuth, sensing radius, and time schedule. To reduce the computational complexity, we propose an optimization coverage scheme with a boundary constraint of eliminating redundancy for DSNs. Combined with Particle Swarm Optimization (PSO) algorithm, a Virtual Angle Boundary-aware Particle Swarm Optimization (VAB-PSO) is designed to reduce the computational burden of optimization problems effectively. The VAB-PSO algorithm generates the boundary constraint position between the sensors according to the relationship among the angles of different sensors, thus obtaining the boundary of particle search and restricting the search space of the algorithm. Meanwhile, different particles search in complementary space to improve the overall efficiency. Experimental results show that the proposed algorithm with a boundary constraint can effectively improve the coverage and convergence speed of the algorithm.

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