研究目的
Investigating the spectrum trading problem in cognitive radio networks, focusing on maximizing the primary spectrum owner's revenue through optimal contract design under complete and incomplete information about secondary users' service classes.
研究成果
The paper concludes that optimal contract design can significantly enhance the PSO's revenue in cognitive radio networks, especially when targeting the highest SU service class under complete information. For incomplete information scenarios, the proposed heuristic algorithms (SCA and MCA) effectively approximate the optimal results, demonstrating their utility in practical applications. The study also highlights the importance of considering social welfare in spectrum trading schemes.
研究不足
The study is theoretical and relies on simulations, which may not capture all real-world complexities. The proposed algorithms' performance is compared to the optimal case under complete information, but practical implementations may face additional challenges.
1:Experimental Design and Method Selection:
The study employs contract theory to design optimal bandwidth-price contracts for secondary users (SUs) in cognitive radio networks, considering both complete and incomplete information scenarios.
2:Sample Selection and Data Sources:
The analysis involves a primary spectrum owner (PSO) managing a total bandwidth, serving primary users (PUs) under specific service level agreements (SLAs), and allowing SUs to access the spectrum opportunistically or exclusively.
3:List of Experimental Equipment and Materials:
The study is theoretical, focusing on mathematical modeling and simulation, without specifying physical equipment.
4:Experimental Procedures and Operational Workflow:
The PSO designs contracts for SUs, aiming to maximize revenue while considering the impact on PUs. Two heuristic algorithms are proposed for the incomplete information case.
5:Data Analysis Methods:
The performance of the proposed algorithms is evaluated through simulation, comparing the PSO utility and social welfare under various scenarios.
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