Power Allocation Algorithm for an Energy-Harvesting Wireless Transmission System Considering Energy Losses
For an energy-harvesting wireless transmission system, considering that a transmitter which can harvest energy from nature has two kinds of extra energy consumption, circuit consumption and storage losses, the optimization models are set up in this paper for the purpose of maximizing the average thr...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-01-01
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| Series: | Algorithms |
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| Online Access: | http://www.mdpi.com/1999-4893/12/1/25 |
| _version_ | 1811267363360735232 |
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| author | Su Zhao Gang Huang Qi Zhu |
| author_facet | Su Zhao Gang Huang Qi Zhu |
| author_sort | Su Zhao |
| collection | DOAJ |
| description | For an energy-harvesting wireless transmission system, considering that a transmitter which can harvest energy from nature has two kinds of extra energy consumption, circuit consumption and storage losses, the optimization models are set up in this paper for the purpose of maximizing the average throughput of the system within a certain period of time for both a time-invariant channel and time-varying channel. Convex optimization methods such as the Lagrange multiplier method and the KKT (Karush–Kuhn–Tucker) condition are used to solve the optimization problem; then, an optimal offline power allocation algorithm which has a three-threshold structure is proposed. In the three-threshold algorithm, two thresholds can be achieved by using a linear search method while the third threshold is calculated according to the channel state information and energy losses; then, the offline power allocation is based on the three thresholds and energy arrivals. Furthermore, inspired by the optimal offline algorithm, a low-complexity online algorithm with adaptive thresholds is derived. Finally, the simulation results show that the offline power allocation algorithms proposed in this paper are better than other algorithms, the performance of the online algorithm proposed is close to the offline one, and these algorithms can help improve the average throughput of the system. |
| first_indexed | 2024-04-12T21:00:02Z |
| format | Article |
| id | doaj.art-2c38685eed66481eb663d159a452e2d4 |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-04-12T21:00:02Z |
| publishDate | 2019-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-2c38685eed66481eb663d159a452e2d42022-12-22T03:16:51ZengMDPI AGAlgorithms1999-48932019-01-011212510.3390/a12010025a12010025Power Allocation Algorithm for an Energy-Harvesting Wireless Transmission System Considering Energy LossesSu Zhao0Gang Huang1Qi Zhu2The National Mobile Communications Research Laboratory, Southeast University, Nanjing 210003, ChinaThe National Mobile Communications Research Laboratory, Southeast University, Nanjing 210003, ChinaJiangsu Key Lab of Wireless Communications, Nanjing University of Posts and Telecommunications, Nanjing 210003, ChinaFor an energy-harvesting wireless transmission system, considering that a transmitter which can harvest energy from nature has two kinds of extra energy consumption, circuit consumption and storage losses, the optimization models are set up in this paper for the purpose of maximizing the average throughput of the system within a certain period of time for both a time-invariant channel and time-varying channel. Convex optimization methods such as the Lagrange multiplier method and the KKT (Karush–Kuhn–Tucker) condition are used to solve the optimization problem; then, an optimal offline power allocation algorithm which has a three-threshold structure is proposed. In the three-threshold algorithm, two thresholds can be achieved by using a linear search method while the third threshold is calculated according to the channel state information and energy losses; then, the offline power allocation is based on the three thresholds and energy arrivals. Furthermore, inspired by the optimal offline algorithm, a low-complexity online algorithm with adaptive thresholds is derived. Finally, the simulation results show that the offline power allocation algorithms proposed in this paper are better than other algorithms, the performance of the online algorithm proposed is close to the offline one, and these algorithms can help improve the average throughput of the system.http://www.mdpi.com/1999-4893/12/1/25energy harvestingcircuit consumptionstorage lossespower allocation |
| spellingShingle | Su Zhao Gang Huang Qi Zhu Power Allocation Algorithm for an Energy-Harvesting Wireless Transmission System Considering Energy Losses Algorithms energy harvesting circuit consumption storage losses power allocation |
| title | Power Allocation Algorithm for an Energy-Harvesting Wireless Transmission System Considering Energy Losses |
| title_full | Power Allocation Algorithm for an Energy-Harvesting Wireless Transmission System Considering Energy Losses |
| title_fullStr | Power Allocation Algorithm for an Energy-Harvesting Wireless Transmission System Considering Energy Losses |
| title_full_unstemmed | Power Allocation Algorithm for an Energy-Harvesting Wireless Transmission System Considering Energy Losses |
| title_short | Power Allocation Algorithm for an Energy-Harvesting Wireless Transmission System Considering Energy Losses |
| title_sort | power allocation algorithm for an energy harvesting wireless transmission system considering energy losses |
| topic | energy harvesting circuit consumption storage losses power allocation |
| url | http://www.mdpi.com/1999-4893/12/1/25 |
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