Adaptive mechanism design and game theoretic analysis of auction-driven dynamic spectrum access in cognitive radio networks

This paper studies the auction-driven dynamic spectrum access in cognitive radio networks with heterogeneous secondary users, who have different risk attitudes. First, a game theoretic framework is established for auction-driven dynamic spectrum access in cognitive radio networks. The utility functions and bidding strategies of heterogeneous secondary users are defined, and the parameterized auction mechanisms of primary user are also introduced. Then, we formulate the auction-driven dynamic spectrum access problem as a finite discrete game with a mixed- or pure-strategy Nash equilibrium solution. We study the existence and uniqueness properties of the pure-strategy Nash equilibrium in the defined game. Next, we propose a distributed learning automata algorithm (DLA) to attain the Nash equilibrium of the defined game with limited feedback. The adaptive mechanism design is realized in the updating procedure of our DLA algorithm. We further prove that our DLA algorithm converges to a Nash equilibrium of the defined game. Finally, simulation results show that our DLA algorithm is efficient and outperforms the dynamic spectrum access schemes with fixed auction mechanism.