LOW COMPLEXITY POWER ALLOCATION SCHEME IN COGNITIVE RADIO SENSOR NETWORKS TO RAISE ENERGY EFFICIENCY

2018 ◽  
Vol 18 (2) ◽  
pp. 251-267
Author(s):  
Mehdi Masoodi ◽  
Mohsen Maesoumi ◽  
Ehsan Akbari Sekehravani
Keyword(s):  
Energy Efficiency ◽  
Cognitive Radio ◽  
Sensor Networks ◽  
Power Allocation ◽  
Low Complexity ◽  
Allocation Scheme ◽  
Cognitive Radio Sensor Networks

scholarly journals Energy-Efficient Cognitive Radio Sensor Networks: Parametric and Convex Transformations

2021 ◽  
Author(s):  
Muhammad Naeem ◽  
Kandasamy Illanko ◽  
Ashok Karmokar ◽  
Alagan Anpalagan ◽  
Muhammad Jaseemuddin
Keyword(s):  
Cognitive Radio ◽  
Sensor Networks ◽  
Constrained Optimization ◽  
Power Allocation ◽  
Energy Efficient ◽  
Optimization Problem ◽  
Numerical Solutions ◽  
Sensor Nodes ◽  
Constrained Optimization Problem ◽  
Cognitive Radio Sensor Networks

Designing energy-efficient cognitive radio sensor networks is important to intelligently use battery energy and to maximize the sensor network life. In this paper, the problem of determining the power allocation that maximizes the energy-efficiency of cognitive radio-based wireless sensor networks is formed as a constrained optimization problem, where the objective function is the ratio of network throughput and the network power. The proposed constrained optimization problem belongs to a class of nonlinear fractional programming problems. Charnes-Cooper Transformation is used to transform the nonlinear fractional problem into an equivalent concave optimization problem. The structure of the power allocation policy for the transformed concave problem is found to be of a water-filling type. The problem is also transformed into a parametric form for which a ε-optimal iterative solution exists. The convergence of the iterative algorithms is proven, and numerical solutions are presented. The iterative solutions are compared with the optimal solution obtained from the transformed concave problem, and the effects of different system parameters (interference threshold level, the number of primary users and secondary sensor nodes) on the performance of the proposed algorithms are investigated.

Joint Spectrum Sensing and Data Transmission Optimization for Energy Efficiency in Cognitive Radio Sensor Networks: A Dynamic Cooperative Method

2015 ◽  
Vol 19 (2) ◽  
pp. 197-204 ◽  
Author(s):  
Yi Li ◽  
◽  
Jun Peng ◽  
Fu Jiang ◽  
Kaiyang Liu ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Cognitive Radio ◽  
Sensor Networks ◽  
Spectrum Sensing ◽  
Data Transmission ◽  
Cooperative Spectrum Sensing ◽  
Optimization Method ◽  
Joint Optimization ◽  
Transmission Scheme ◽  
Cognitive Radio Sensor Networks

To address the inherent energy constraint in cognitive radio sensor networks, a novel joint optimization method of spectrum sensing and data transmission for energy efficiency is investigated in this paper. To begin with, a cooperative spectrum sensing scheme based on dynamic censoring is employed to shorten sensing time and save unnecessary spectrum sensing energy. Then to jointly optimize the energy efficiency, the distortion constrained probabilistic transmission scheme is utilized. Afterwards the sensing threshold solving issue can be formulated as a nonlinear minmax optimization problem with the detection probability and false alarm probability constraints. Solving by the Matlab software with the free OPTI toolbox, simulation results demonstrate that significant energy can be saved via the the proposed joint optimization method in various mobile cloud scenarios.

scholarly journals Energy-Efficient Cognitive Radio Sensor Networks: Parametric and Convex Transformations

2021 ◽  
Author(s):  
Muhammad Naeem ◽  
Kandasamy Illanko ◽  
Ashok Karmokar ◽  
Alagan Anpalagan ◽  
Muhammad Jaseemuddin
Keyword(s):  
Cognitive Radio ◽  
Sensor Networks ◽  
Constrained Optimization ◽  
Power Allocation ◽  
Energy Efficient ◽  
Optimization Problem ◽  
Numerical Solutions ◽  
Sensor Nodes ◽  
Constrained Optimization Problem ◽  
Cognitive Radio Sensor Networks

Designing energy-efficient cognitive radio sensor networks is important to intelligently use battery energy and to maximize the sensor network life. In this paper, the problem of determining the power allocation that maximizes the energy-efficiency of cognitive radio-based wireless sensor networks is formed as a constrained optimization problem, where the objective function is the ratio of network throughput and the network power. The proposed constrained optimization problem belongs to a class of nonlinear fractional programming problems. Charnes-Cooper Transformation is used to transform the nonlinear fractional problem into an equivalent concave optimization problem. The structure of the power allocation policy for the transformed concave problem is found to be of a water-filling type. The problem is also transformed into a parametric form for which a ε-optimal iterative solution exists. The convergence of the iterative algorithms is proven, and numerical solutions are presented. The iterative solutions are compared with the optimal solution obtained from the transformed concave problem, and the effects of different system parameters (interference threshold level, the number of primary users and secondary sensor nodes) on the performance of the proposed algorithms are investigated.

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