Power Control and Routing Selection for Throughput Maximization in Energy Harvesting Cognitive Radio Networks

被引:3
作者
He, Xiaoli [1 ,2 ]
Jiang, Hong [1 ]
Song, Yu [1 ,3 ]
Owais, Muhammad [4 ]
机构
[1] South West Univ Sci & Technol, Sch Informat Engn, Mianyang 621010, Sichuan, Peoples R China
[2] Sichuan Univ Sci & Engn, Sch Comp Sci, Zigong 643000, Peoples R China
[3] Sichuan Univ Sci & Engn, Dept Network Informat Management Ctr, Zigong 643000, Peoples R China
[4] Alhamd Islamic Univ, Balochistan, Pakistan
来源
CMC-COMPUTERS MATERIALS & CONTINUA | 2020年 / 63卷 / 03期
基金
中国国家自然科学基金;
关键词
Cognitive radio networks; power control; routing selection; energy harvesting; game theory; amplify-and-forward (AF); throughput; RESOURCE-ALLOCATION; MINIMIZATION;
D O I
10.32604/cmc.2020.09908
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper investigates the power control and routing problem in the communication process of an energy harvesting (EH) multi-hop cognitive radio network (CRN). The secondary user (SU) nodes (i.e., source node and relay nodes) harvest energy from the environment and use the energy exclusively for transmitting data. The SU nodes (i.e., relay nodes) on the path, store and forward the received data to the destination node. We consider a real world scenario where the EH-SU node has only local causal knowledge, i.e., at any time, each EH-SU node only has knowledge of its own EH process, channel state and currently received data. In order to study the power and routing issues, an optimization problem that maximizes path throughput considering quality of service (QoS) and available energy constraints is proposed. To solve this optimization problem, we propose a hybrid game theory routing and power control algorithm (HGRPC). The EH-SU nodes on the same path cooperate with each other, but EH-SU nodes on the different paths compete with each other. By selecting the best next hop node, we find the best strategy that can maximize throughput. In addition, we have established four steps to achieve routing, i.e., route discovery, route selection, route reply, and route maintenance. Compared with the direct transmission, HGRPC has advantages in longer distances and higher hop counts. The algorithm generates more energy, reduces energy consumption and increases predictable residual energy. In particular, the time complexity of HGRPC is analyzed and its convergence is proved. In simulation experiments, the performance (i.e., throughput and bit error rate (BER)) of HGRPC is evaluated. Finally, experimental results show that HGRPC has higher throughput, longer network life, less latency, and lower energy consumption.
引用
收藏
页码:1273 / 1296
页数:24
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