Resource Allocation in Cognitive Radio Wireless Sensor Networks with Energy Harvesting
<p>Cognitive radio wireless sensor network (CRWSN) system model.</p> "> Figure 2
<p>Harvest-then-transmit mode.</p> "> Figure 3
<p>(<b>a</b>) Variation of <math display="inline"><semantics> <mrow> <msubsup> <mi>u</mi> <mi>i</mi> <mo>*</mo> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> with time t for the open loop solution; (<b>b</b>) variation of <math display="inline"><semantics> <mrow> <mi>x</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> with time t for the open loop solution.</p> "> Figure 4
<p>(<b>a</b>) Variation of <math display="inline"><semantics> <mrow> <msubsup> <mi>u</mi> <mi>i</mi> <mo>*</mo> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> with time t for the feedback solution; (<b>b</b>) variation of <math display="inline"><semantics> <mrow> <mi>x</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> with time t for the feedback solution.</p> "> Figure 5
<p>(<b>a</b>) Variation of <math display="inline"><semantics> <mrow> <msubsup> <mi>u</mi> <mi>i</mi> <mo>*</mo> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> for the feedback solution under infinite horizon at <span class="html-italic">t</span> = 10; (<b>b</b>) variation of <math display="inline"><semantics> <mrow> <msubsup> <mi>u</mi> <mi>i</mi> <mo>*</mo> </msubsup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> for the feedback solution under infinite horizon at the end of the game.</p> ">
Abstract
:1. Introduction
2. System Model and Problem Formulation
2.1. System Model
2.2. Game Formulation
- Players: The set of SUs in the proposed CRWSN are the players of the differential game;
- State: The system state of the proposed CRWSN is the capacity of the spectrum resource the PU wants to lease;
- Strategy: The strategy of each SU is the leased spectrum resource from the PU. Accordingly, the strategy set can be denoted as .
3. Game Analysis
3.1. Open Loop Nash Equilibrium
3.2. Feedback Nash Equilibrium under Finite Horizon
3.3. Feedback Nash Equilibrium under Infinite Horizon
4. Numerical Simulations
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Parameters | i | r | T | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Value | 1 | 0.4 | 0.8 | 0.1 | 0.1 | 0.3 | 0.028 | 0.15 | 0.2 | −0.15 | −0.6 | 0.1 | 100 |
Value | 2 | 0.5 | 0.4 | 0.3 | 0.4 | 0.5 | 0.03 | 0.15 | 0.2 | −0.15 | −0.5 | 0.6 | 100 |
Value | 3 | 0.6 | 0.3 | 0.6 | 0.7 | 0.7 | 0.034 | 0.15 | 0.2 | −0.15 | −0.4 | 0.8 | 100 |
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Xu, H.; Gao, H.; Zhou, C.; Duan, R.; Zhou, X. Resource Allocation in Cognitive Radio Wireless Sensor Networks with Energy Harvesting. Sensors 2019, 19, 5115. https://doi.org/10.3390/s19235115
Xu H, Gao H, Zhou C, Duan R, Zhou X. Resource Allocation in Cognitive Radio Wireless Sensor Networks with Energy Harvesting. Sensors. 2019; 19(23):5115. https://doi.org/10.3390/s19235115
Chicago/Turabian StyleXu, Haitao, Hongjie Gao, Chengcheng Zhou, Ruifeng Duan, and Xianwei Zhou. 2019. "Resource Allocation in Cognitive Radio Wireless Sensor Networks with Energy Harvesting" Sensors 19, no. 23: 5115. https://doi.org/10.3390/s19235115
APA StyleXu, H., Gao, H., Zhou, C., Duan, R., & Zhou, X. (2019). Resource Allocation in Cognitive Radio Wireless Sensor Networks with Energy Harvesting. Sensors, 19(23), 5115. https://doi.org/10.3390/s19235115