Market-Equilibrium, Competitive, and Cooperative Pricing for Spectrum Sharing in Cognitive Radio Networks: Analysis and Comparison

摘要

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.