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A Metaheuristic Based Approach for Threshold Optimization for Spectrum Sensing in Cognitive Radio Networks


Garima Mahendru*, Anil K Shukla and L M Patnaik   Pages 1 - 9 ( 9 )


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.


Spectrum Sensing, Noise Uncertainty, Adaptive Threshold, Particle Swarm Optimization, Minimal Error Rate, Detection Probability


Amity School of Engineering & Technology, Amity University, Uttar Pradesh, India, Amity School of Engineering & Technology, Amity University, Uttar Pradesh, India, Indian Institute of Science , Bangalore

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