Robustness-Based Transmission Strategy for Wireless-Powered Communication Networks

In the wireless powered communication network (WPCN), the deployed nodes that harvest energy from the power station (PS) and then transmit data to the access point (AP) may waste wireless information transmission (WIT) opportunities due to the suffered faults. Confronting this dilemma, we propose a...

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Bibliographic Details
Published in:Wireless communications and mobile computing 2022-10, Vol.2022, p.1-10
Main Authors: Liu, Xiaoying, Wang, Aodi, Xu, Bin, Zheng, Kechen, Yao, Xinwei
Format: Article
Language:English
Subjects:
Algorithms
Communication networks
Energy efficiency
Energy harvesting
Internet of Things
Markov analysis
Markov chains
Nodes
Power plants
Probability
Probability distribution
Robustness (mathematics)
Sensors
Tradeoffs
Wireless communications
Wireless networks
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Summary:In the wireless powered communication network (WPCN), the deployed nodes that harvest energy from the power station (PS) and then transmit data to the access point (AP) may waste wireless information transmission (WIT) opportunities due to the suffered faults. Confronting this dilemma, we propose a novel metric robustness, defined as the number of available nodes for WIT, and evaluate its impact on the throughput. To optimize the tradeoff between the throughput and robustness, we design an energy threshold approach, where the robustness decreases with the energy threshold and the throughput first increases and then decreases with it due to the tradeoff between the WIT rate and WIT opportunities. In the designed approach, we formulate the nodes with different energy states as Markov chain processes and prove the existence of steady-state probability distribution. Moreover, in the scenario with high robustness by the designed approach, we select the nodes with higher energy for WIT by the improved k-means++ algorithm, in order to increase the throughput. Finally, simulations validate the theoretical analysis of the throughput and robustness performances under the improved k-means++ algorithm and show that, compared with the comparison algorithms, the improved k-means++ algorithm achieves similar throughput and robustness performances with low computational complexity.
ISSN:1530-8669
1530-8677
DOI:10.1155/2022/2532905