An efficient spectrum management mechanism for cognitive radio networks
IM'09 Proceedings of the 11th IFIP/IEEE international conference on Symposium on Integrated Network Management
Wireless mesh networking games
GameNets'09 Proceedings of the First ICST international conference on Game Theory for Networks
IEEE Transactions on Communications
Economic Approaches for Cognitive Radio Networks: A Survey
Wireless Personal Communications: An International Journal
Wireless Personal Communications: An International Journal
An agent-based simulation model of a market for dynamic spectrum access
Proceedings of the 4th International Conference on Cognitive Radio and Advanced Spectrum Management
Optimizing spectrum trading in cognitive mesh network using machine learning
Journal of Electrical and Computer Engineering - Special issue on Resource Allocation in Communications and Computing
Wireless Personal Communications: An International Journal
A stackelberg game for spectrum leasing in cooperative cognitive radio networks
International Journal of Automation and Computing
Power Control and Allocation for MIMO Broadcast Channels in Cognitive Radio Networks
Wireless Personal Communications: An International Journal
Wireless Personal Communications: An International Journal
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In a cognitive radio network, frequency spectrum can be shared between primary (or licensed) users and secondary (or unlicensed) users, where the secondary users pay the primary users (or primary service provider) for radio resource usage. This is referred to as spectrum trading. In spectrum trading, pricing is a key issue of interest to primary service providers (i.e., spectrum sellers) as well as to secondary service providers (i.e., spectrum buyers). In a cognitive radio network, pricing model for spectrum sharing depends on the objective of spectrum trading, and therefore, the behaviors of spectrum sellers and spectrum buyers. In this paper, we investigate three different pricing models, namely, market-equilibrium, competitive, and cooperative pricing models for spectrum trading in a cognitive radio environment. In these pricing models, the primary service providers have different behaviors (i.e., competitive and cooperative behaviors) to achieve different objectives of spectrum trading. Specifically, in marketequilibrium pricing model, the objective of spectrum trading is to satisfy spectrum demand from the secondary users, and there is neither competition nor cooperation among primary service providers. In the competitive pricing, the objective is to maximize the individual profit, and there is competition among primary service providers. In cooperative pricing, the objective of spectrum trading is to maximize the total profit, and cooperation exists among primary service providers. We propose distributed algorithms to achieve the pricing solutions of these different pricing models and analyze stability of these distributed algorithms. We perform extensive performance analysis of these pricing algorithms considering different aspects such as profit of the primary service providers, stability region, and impact of number of primary service providers, which reveals interesting insights into the spectrum trading problem.