Maximizing Average Throughput of Cooperative Cognitive Radio Networks Based on Energy Harvesting

Energy harvesting (EH) and cooperative communication techniques have been widely used in cognitive radio networks. However, most studies on throughput in energy-harvesting cooperative cognitive radio networks (EH-CCRNs) are end-to-end, which ignores the overall working state of the network. For the...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2022-11, Vol.22 (22), p.8921
Main Authors: Wang, Yaqing, Chen, Shiyong, Wu, Yucheng, Zhao, Chengxin
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Cognitive radio
Convex analysis
cooperative cognitive radio network
Energy consumption
Energy conversion (Power resources)
Energy harvesting
Mathematical optimization
network average throughput
Optimization
Polynomials
Radio networks
Receivers & amplifiers
Resource allocation
RF energy harvesting
Wireless communications
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Summary:Energy harvesting (EH) and cooperative communication techniques have been widely used in cognitive radio networks. However, most studies on throughput in energy-harvesting cooperative cognitive radio networks (EH-CCRNs) are end-to-end, which ignores the overall working state of the network. For the above problems, under the premise of prioritizing the communication quality of short-range users, this paper focuses on the optimization of the EH-CCRN average throughput, with energy and transmission power as constraints. The formulated problem was an unsolved non-deterministic polynomial-time hardness (NP-hard) problem. To make it tractable to solve, a multi-user time-power resource allocation algorithm (M-TPRA) is proposed, which is based on sub-gradient descent and unary linear optimization methods. Simulation results show that the M-TPRA algorithm can improve the average throughput of the network. In addition, the energy consumed by executing the M-TPRA algorithm is analyzed.
ISSN:1424-8220
1424-8220
DOI:10.3390/s22228921