Joint Resource Allocation of Spectrum Sensing and Energy Harvesting in an Energy-Harvesting-Based Cognitive Sensor Network
Xin Liu
1
School of Information and Communication Engineering, Dalian University of Technology, Dalian 116024, China
2
College of Information Engineering, Zhejiang University of Technology, Hangzhou 310014, China
3
Communication Research Center, Harbin Institute of Technology, Harbin 150080, China
*
Author to whom correspondence should be addressed.
Academic Editor: Davide Brunelli
Sensors 2017, 17(3), 600; https://doi.org/10.3390/s17030600
Received: 11 February 2017 / Revised: 10 March 2017 / Accepted: 13 March 2017 / Published: 16 March 2017
(This article belongs to the Special Issue Energy Harvesting Sensors for Long Term Applications in the IoT Era)
The cognitive sensor (CS) can transmit data to the control center in the same spectrum that is licensed to the primary user (PU) when the absence of the PU is detected by spectrum sensing. However, the battery energy of the CS is limited due to its small size, deployment in atrocious environments and long-term working. In this paper, an energy-harvesting-based CS is described, which senses the PU together with collecting the radio frequency energy to supply data transmission. In order to improve the transmission performance of the CS, we have proposed the joint resource allocation of spectrum sensing and energy harvesting in the cases of a single energy-harvesting-based CS and an energy-harvesting-based cognitive sensor network (CSN), respectively. Based on the proposed frame structure, we have formulated the resource allocation as a class of joint optimization problems, which seek to maximize the transmission rate of the CS by jointly optimizing sensing time, harvesting time and the numbers of sensing nodes and harvesting nodes. Using the half searching method and the alternating direction optimization, we have achieved the sub-optimal solution by converting the joint optimization problem into several convex sub-optimization problems. The simulation results have indicated the predominance of the proposed energy-harvesting-based CS and CSN models.
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Liu, X.; Lu, W.; Ye, L.; Li, F.; Zou, D. Joint Resource Allocation of Spectrum Sensing and Energy Harvesting in an Energy-Harvesting-Based Cognitive Sensor Network. Sensors 2017, 17, 600.
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