Optimal spectrum sensing and transmission power allocation in energy-efficiency multichannel cognitive radio with energy harvesting

2015 ◽  
Vol 30 (5) ◽  
pp. e3044 ◽  
Author(s):  
Xin Liu ◽  
Min Jia ◽  
Xue-mai Gu ◽  
Jun-hua Yan ◽  
Jian-jiang Zhou
Keyword(s):  
Energy Efficiency ◽  
Cognitive Radio ◽  
Energy Harvesting ◽  
Power Allocation ◽  
Spectrum Sensing ◽  
Transmission Power

scholarly journals Energy Efficiency Analysis in Modified GoF Spectrum Sensing-Based AF Relay Cooperative Cognitive Sensor Network with Energy Harvesting

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yin Mi ◽  
Guangyue Lu ◽  
Wenbin Gao
Keyword(s):  
Energy Efficiency ◽  
Energy Harvesting ◽  
Sensor Network ◽  
Spectrum Sensing ◽  
Closed Form Expression ◽  
Initial Time ◽  
Maximization Problem ◽  
Power Splitting ◽  
Transmission Power ◽  
Af Relay

In this paper, we propose a joint sensing duration and transmission power allocation scheme to maximize the energy efficiency (EE) of the secondary user (SU) in a cooperative cognitive sensor network (CSN). At the initial time slot of the frame, the secondary transmitter (ST) performs energy harvesting (EH) and spectrum sensing simultaneously using power splitting (PS) protocol. The modified goodness of fit (GoF) spectrum sensing algorithm is employed to detect the licensed spectrum, which is not sensitive to an inaccurate noise power estimate. Based on the imperfect sensing results, the ST will act as an amplify-and-forward (AF) relay and assist in transmission of the primary user (PU) or transmit its own data. The SU’s EE maximization problem is constructed under the constraints of meeting energy causality, sensing reliability, and PU’s quality of service (QoS) requirement. Since the SU’s EE function is a nonconvex problem and difficult to solve, we transform the original problem into a tractable convex one with the aid of Dinkelbach’s method and convex optimization technique by applying a nonlinear fractional programming. The closed-form expression of the ST’s transmission power is also derived through Karush-Kuhn-Tucker (KKT) and gradient method. Simulation results show that our scheme is superior to the existing schemes.

scholarly journals Optimum energy harvesting model for bidirectional cognitive radio networks

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Mohammad Kamrul Hasan ◽  
Md. Monwar J. Chowdhury ◽  
Shakil Ahmed ◽  
Saifur R. Sabuj ◽  
Jamel Nibhen ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Cognitive Radio ◽  
Energy Harvesting ◽  
Outage Probability ◽  
Primary User ◽  
Battery Life ◽  
Transmission Power ◽  
Wireless Devices ◽  
Harvesting Technique ◽  
Power And Energy

AbstractWireless devices’ energy efficiency and spectrum shortage problem has become a key concern worldwide as the number of wireless devices increases at an unparalleled speed. Wireless energy harvesting technique from traditional radio frequency signals is suitable for extending mobile devices’ battery life. This paper investigates a cognitive radio network model where primary users have their specific licensed band, and secondary users equipped with necessary hardware required for energy harvesting can use the licensed band of the primary user by smart sensing capability. Analytical expressions for considered network metrics, namely data rate, outage probability, and energy efficiency, are derived for uplink and downlink scenarios. In addition, optimal transmission power and energy harvesting power are derived for maximum energy efficiency in downlink and uplink scenarios. Numerical results show that outage probability improves high transmission power in the downlink scenario and high harvested power in the uplink scenario. Finally, the result shows that energy efficiency improves using optimum transmission power and energy harvesting power for downlink and uplink scenarios.

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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