Open Access

ARTICLE

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

Xiaoli He^{1, 2}, Hong Jiang^{1, *}, Yu Song^{1, 3}, Muhammad Owais⁴

1 School of Information Engineering, South West University of Science and Technology, Mianyang, 621010, China.
2 School of Computer Science, Sichuan University of Science and Engineering, Zigong, 643000, China.
3 Department of Network Information Management Center, Sichuan University of Science and Engineering, Zigong, 643000, China.
4 Alhamd Islamic University, Balochistan, Pakistan.

* Corresponding Author: Hong Jiang, Email: .

Computers, Materials & Continua 2020, 63(3), 1273-1296. https://doi.org/10.32604/cmc.2020.09908

Received 26 January 2020; Accepted 24 February 2020; Issue published 30 April 2020

Abstract

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.

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Citations