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Spectrum Trading in Cognitive Radio Networks: A Contract-Theoretic Modeling Approach

Spectrum Trading in Cognitive Radio Networks: A Contract-Theoretic Modeling Approach. Lin Gao, Xinbing Wang, Youyun Xu, Qian Zhang Shanghai Jiao Tong University, China HKUST, HongKong. Outline. Introduction Background Objective System Model and Contract Formulation

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Spectrum Trading in Cognitive Radio Networks: A Contract-Theoretic Modeling Approach

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  1. Spectrum Trading in Cognitive Radio Networks: A Contract-Theoretic Modeling Approach Lin Gao, Xinbing Wang, Youyun Xu, Qian Zhang Shanghai Jiao Tong University, China HKUST, HongKong

  2. Outline • Introduction • Background • Objective • System Model and Contract Formulation • Feasibility of Contract • Optimality of Contract • Simulations and Conclusions Spectrum Trading in Cognitive Radio Networks: A Contract-Theoretic Modeling Approach

  3. Motivation • Currently, wireless systems suffer from the inefficiency in spectrum usage. Dynamic spectrum access (DSA) is a promising paradigm to achieve efficient utilization of the spectrum resource. • The primary spectrum owners (POs) may lease their excess spectrum bands to the secondary users (SUs) for enhanced profit – Spectrum Trading. • Spectrum trading mechanism designing becomes complicated when POs have incomplete information about SUs. Spectrum Trading in Cognitive Radio Networks: A Contract-Theoretic Modeling Approach

  4. Objectives • We consider the problem of spectrum trading with incomplete information, where single (monopolist) PO selling his idle spectrum to multiple SUs. • We first consider the issue of quality discrimination for the spectrum trading with multiple SU-types. • We introduce the concept of contract into the quality discrimination spectrum trading, and design a monopolist dominated quality-price contract. • We propose the necessary and sufficient conditions for the feasible contract, which is incentive compatible (IC) and individually rational (IR) for each SU. • We derive the optimal contract which is feasible and maximizes the revenue of the PO. We find that the feasible contract can naturally reduce the interference between the primary network and secondary network. Spectrum Trading in Cognitive Radio Networks: A Contract-Theoretic Modeling Approach

  5. Outline • Introduction • System Model and Contract Formulation • System Model • Problem Formulation • Feasibility of Contract • Optimality of Contract • Simulations and Conclusions Spectrum Trading in Cognitive Radio Networks: A Contract-Theoretic Modeling Approach

  6. System Model Monopoly Spectrum Market Model with N=9 SUs within 3 Types. Spectrum Trading in Cognitive Radio Networks: A Contract-Theoretic Modeling Approach

  7. System Model An illustration of the optimal qualities and prices for PO in different scenario. Spectrum Trading in Cognitive Radio Networks: A Contract-Theoretic Modeling Approach

  8. Problem Formulation Spectrum Trading in Cognitive Radio Networks: A Contract-Theoretic Modeling Approach

  9. Outline • Introduction • System Model and Contract Formulation • Feasibility of Contract • Necessary and Sufficient Conditions • Optimality of Contract • Simulations and Conclusions Spectrum Trading in Cognitive Radio Networks: A Contract-Theoretic Modeling Approach

  10. Feasibility of Contract Spectrum Trading in Cognitive Radio Networks: A Contract-Theoretic Modeling Approach

  11. Feasibility of Contract An illustration of the feasible price range characterized by c.1 and c.2 Spectrum Trading in Cognitive Radio Networks: A Contract-Theoretic Modeling Approach

  12. Outline • Introduction • System Model and Contract Formulation • Feasibility of Contract • Optimality of Contract • Optimal Conditions • Convergence • Simulations and Conclusions Spectrum Trading in Cognitive Radio Networks: A Contract-Theoretic Modeling Approach

  13. Optimality of Contract Spectrum Trading in Cognitive Radio Networks: A Contract-Theoretic Modeling Approach

  14. Optimality of Contract An illustration of the best price assignment characterized by c.1 and c.2 Spectrum Trading in Cognitive Radio Networks: A Contract-Theoretic Modeling Approach

  15. Converge to Optimal Quality • Dynamic Algorithm for {qt } Illustrating the process of the dynamic algorithm Spectrum Trading in Cognitive Radio Networks: A Contract-Theoretic Modeling Approach

  16. Continuous-SU-Type Model Spectrum Trading in Cognitive Radio Networks: A Contract-Theoretic Modeling Approach

  17. Outline • Introduction • System Model and Contract Formulation • Feasibility of Contract • Optimality of Contract • Simulations and Conclusions • Simulation Results • Conclusions & Future Works Spectrum Trading in Cognitive Radio Networks: A Contract-Theoretic Modeling Approach

  18. Simulation Results Quality Assignment Price Assignment The best quality set and price set in the optimal contract Spectrum Trading in Cognitive Radio Networks: A Contract-Theoretic Modeling Approach

  19. Simulation Results The social surplus and the revenues of the PO in the optimal contracts. S* : Social Surplus in integrated optimal solution So: Social Surplus with Optimal Contract S* > So R* : PU’s revenue in integrated optimal solution Ro: PU’s revenue with Optimal Contract R* << Ro Spectrum Trading in Cognitive Radio Networks: A Contract-Theoretic Modeling Approach

  20. Conclusions • In this paper, we study the problem of spectrum trading with incomplete information, where single (monopolist) PO selling his idle spectrum to multiple SUs. • We introduce the concept of contract into the quality discrimination spectrum trading, and design a monopolist dominated quality-price contract. • We propose the necessary and sufficient conditions for the feasible contract, which is incentive compatible (IC) and individually rational (IR) for each SU. • We derive the optimal contract which is feasible and maximizes the revenue of the PO. We find that the feasible contract can naturally reduce the interference between the primary network and secondary network. Spectrum Trading in Cognitive Radio Networks: A Contract-Theoretic Modeling Approach

  21. Thank you !

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