scholarly journals Spectrum Sensing using Enhanced Restricted Boltzmann Machine for Cognitive Radio Network

2020 ◽  
Vol 9 (11) ◽  
pp. 271-278
Keyword(s):  
Energy Efficiency ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Detection Probability ◽  
Cognitive Radio Network ◽  
Restricted Boltzmann Machine ◽  
Machine Learning Techniques ◽  
Delivery Ratio ◽  
Boltzmann Machine ◽  
Radio Network

Cognitive radio network (CRN) came in to existence as a promising solution to tackle issues due to scarcity of spectrum. Spectrum sensing plays an important role for maximizing the spectrum utilization where spectrum of the primary users (PU) is sensed by the secondary user (SU) at particular time and space. Researchers have presented machine learning techniques for spectrum sensing, though, challenges exists for the improvement in the throughput, energy efficiency, detection probability and delivery ratio. In this paper, an enhanced restricted Boltzmann machine (ERBM) is presented for spectrum sensing based on RBM. Particle Swarm Optimization (PSO) is incorporated for enhancing the performance of spectrum sensing and computation of optimal momentum coefficient of RBM. Simulation results shows that the performance of the proposed spectrum sensing technique is comparable to the existing techniques in terms of throughput, energy efficiency and detection probability and delivery ratio.

scholarly journals Energy efficient spectrum sensing for cognitive radio network using artificial bee colony algorithm

2018 ◽  
Vol 7 (4) ◽  
pp. 2319 ◽  
Author(s):  
Geoffrey Eappen ◽  
Dr T. Shankar
Keyword(s):  
Energy Efficiency ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Energy Efficient ◽  
Artificial Bee Colony ◽  
Cognitive Radio Network ◽  
Optimization Technique ◽  
Radio Network ◽  
Abc Algorithm ◽  
Bee Colony

In this paper Artificial Bee Colony (ABC) algorithm based optimization of energy efficiency for spectrum sensing in a Cognitive Radio Network (CRN) is implemented. ABC algorithm which is an efficient optimization technique is used for optimizing energy efficiency func-tion derived for cognitive users, where energy efficiency function is derived as the dependency on spectrum sensing time and the transmis-sion power. Energy efficiency optimized by ABC is compared with Particle Swarm Optimization (PSO) based technique. Simulation results shows that with ABC it is able to achieve more energy efficient spectrum sensing as compared to PSO optimized with a margin of 33% efficiency over PSO.  

Energy Efficiency in Cognitive Radio Network: Green Technology Towards Next Generation Networks

2016 ◽  
Vol 5 (1) ◽  
pp. 45
Author(s):  
Seetaiah Kilaru ◽  
Adithya Gali
Keyword(s):  
Energy Efficiency ◽  
Cognitive Radio ◽  
Smart Grid ◽  
Power Allocation ◽  
Spectrum Sensing ◽  
Cognitive Radio Network ◽  
Power Grid ◽  
Mobile Network ◽  
Radio Network ◽  
Data Rates

Energy efficiency of mobile network is always a challenging task. From the past decade, it is observable that the users who are using multimedia services are increasing in rapid way. These multimedia applications require higher data rates. High data rates will consume more energy of mobile network, which results poor energy efficiency. To meet higher data rates and to achieve energy efficiency, Cognitive Mobile Network with small cell model was explained in this paper. Dynamics of the power grid also have significant impact on mobile networks, hence smart grid implementation was proposed instead of traditional power grid. Most of the existed studies on cognitive mobile network focussed on spectrum sensing only. This paper focussed on cognitive radio network implementation by considering spectrum sensing and smart grid environment. An iterative algorithm was proposed to attain equillibrium condition to the problem. Interference management and energy efficient power allocation were achieved with the introduction of smart grid. Simulation results proved that optimum power allocation and energy efficiency are possible with the introduction of smart grid in cognitive network.

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