A New Spectrum Sensing Strategy for Dynamic Primary Users in Cognitive Radio

Düzenli̇, Timur; Akay, Olcay

doi:10.1109/lcomm.2016.2527640

Cited by 19 publications

(11 citation statements)

References 12 publications

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“…two-state random process with an exponential holding times. Spectrum sensing with multiple status changes in PU traffic was analyzed in [23], while the spectrum sensing strategy for dynamic PUs in CR modeled using a two-state Markov chain was carried out in [24], [25].…”

Section: A Current State Of the Art And Motivationmentioning

confidence: 99%

Long Short-Term Memory Based Spectrum Sensing Scheme for Cognitive Radio Using Primary Activity Statistics

2020

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The cognitive radio (CR) network consists of primary users (PUs) and secondary users (SUs). The SUs in the CR network senses the spectrum band to opportunistically access the white space. Exploiting the white spaces helps to improve the spectrum efficiency. Owing to the excellent learning ability of machine learning/deep learning framework, many works in the recent past have applied shallow/deep multi-layer perceptron approach for spectrum sensing. However, the multi-layer perceptron networks are not well suited for time-series data due to the absence of memory elements. On the other hand, long short-term memory (LSTM) network, an improved version of Recurrent neural network is well suited for time-series data. In this paper, we propose an LSTM based spectrum sensing (LSTM-SS), which learns the implicit features from the spectrum data, for instance, the temporal correlation (i.e., the correlation between the present and past timestamp).Moreover, the CR systems also exploits the PU activity statistics to improve the CR performance. In this context, we compute the PU activity statistics like on and off period duration, duty cycle and propose the PU activity statistics based spectrum sensing (PAS-SS) to enhance the sensing performance. The proposed sensing schemes are validated on the spectrum data of various radio technologies acquired using an experimental test-bed setup. The proposed LSTM-SS scheme is compared with the state of the art spectrum sensing techniques. Experimental results indicate that the proposed schemes has improved detection performance and classification accuracy at low signal to noise ratio regimes. We notice that the improvement achieved is at the cost of longer training time and a nominal increase in execution time.

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Section: A Current State Of the Art And Motivationmentioning

confidence: 99%

Long Short-Term Memory Based Spectrum Sensing Scheme for Cognitive Radio Using Primary Activity Statistics

2020

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“…This cluster head reduce the outage probability. [2] Consider the Spectrum sharing scenario among the primary user (PU), and cognitive radio network. The primary network is the cognitive base station (CBS).…”

Section: Related Workmentioning

confidence: 99%

“…The channel coefficient is represented by f i , p. It lies between primary receiver p link and secondary transmitter i link. The coefficients of the channel (h j,i , g p , ) are For the Secondary transmissions the transmit power can be enabled by splitting the frequency band which have some limitations as expressed in equation (2). Therefore a simple uniform power allocation method is used.…”

Section: Figure 1: Spectrum Sharing In Cognitive Radiomentioning

confidence: 99%

Heuristic Greedy Method for Spectrum Sensing in Cognitive Radio Network

Muthukumaran¹,

Omkumar²

2019

IJEAT

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In recent years, radio frequency spectrum in wireless communication is not effectively utilized. To utilize the spectrum effectively, an optimistic technology called "Cognitive Radio network" used. It is the best preferable next generation wireless networks. Using DSA (Dynamic Spectrum Access) approaches, it shares the spectrum effectively between the primary and secondary users. It allows the secondary users to use the spectrum by dynamic spectrum sharing algorithms. When the primary users and secondary users are using same frequency band and transmitting simultaneously, there is a spectrum underlay problem in the network. A novel heuristic greedy algorithm proposed for improving the performance parameters of cognitive radio network using co-operative spectrum sensing.

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“…Although multiple PUs can arrive or depart randomly, this assumption is still reasonable based on the long time average scenario of observed PU behavior. One may refer to [ 26 , 27 ] to optimize both the sensing time and sensing period (duty cycle) so that instantaneous sensing performance improves under random arrivals of multiple PUs in cooperative sensing.…”

Section: System Modelmentioning

confidence: 99%

On Optimal Cooperative Sensing with Energy Detection in Cognitive Radio

Bae

Kim

2017

Sensors

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In this paper, we propose an optimal cooperative sensing technique for cognitive radio to maximize sensing performance based on energy detection. In most spectrum sensing research, many cooperation methods have been proposed to overcome the sensitivity of energy detection so that both primary and secondary users are better off in terms of spectral efficiency. However, without assigning a proper sensing threshold to each sensing node, cooperation may not be effective unless the received average primary user signal-to-noise ratio (SNR) is identical. We show that equal threshold energy detection severely degrades sensing performance when cooperative sensing nodes experience diverse average SNRs, and it is not unusual for even single-node sensing to be better than cooperative sensing. To this end, based on the Neyman–Pearson criterion we formulate an optimization problem to maximize sensing performance by using optimized thresholds. Since this is a non-convex optimization problem, we provide a condition that convexifies the problem and thus serves as a sufficient optimality condition. We find that, perhaps surprisingly, in all practical cases one may consider this condition satisfied, and thus optimal sensing performance can be obtained. Through extensive simulations, we demonstrate that the proposed technique achieves a globally optimal solution, i.e., it maximizes the probability of detection under practical operating parameters such as the target probability of false alarm, different SNRs, and the number of cooperative sensing nodes.

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A New Spectrum Sensing Strategy for Dynamic Primary Users in Cognitive Radio

Cited by 19 publications

References 12 publications

Long Short-Term Memory Based Spectrum Sensing Scheme for Cognitive Radio Using Primary Activity Statistics

Long Short-Term Memory Based Spectrum Sensing Scheme for Cognitive Radio Using Primary Activity Statistics

Heuristic Greedy Method for Spectrum Sensing in Cognitive Radio Network

On Optimal Cooperative Sensing with Energy Detection in Cognitive Radio

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