In cognitive networks, how to stimulate cooperation among nodes is very important. However, most existing game-theoretic cooperation stimulation approaches rely on the assumption that the interactions between any pair of players are long-lasting. When this assumption is not true, such as in the well-known Prisoner’s dilemma and the backward induction principle, the unique Nash equilibrium is to always play noncooperatively. In this chapter, we discuss a cooperation stimulation scheme for the scenario in which the number of interactions is finite. This scheme is based on indirect reciprocity game modeling where the key concept is “I help you not because you have helped me but because you have helped others.” The problem of finding the optimal action rule is formulated as a Markov decision process, and a modified value-iteration algorithm is utilized to find the optimal action rule. Using the packet forwarding game as an example, it is shown that with an appropriate cost-to-gain ratio, the strategy of forwarding the number of packets that is equal to the reputation level of the receiver is an evolutionarily stable strategy.

The viability of cooperative communications depends on the willingness of users to help. Therefore, it is important to study incentive issues when designing such systems. In this chapter, we discuss a cooperation stimulation scheme for multiuser cooperative communications using an indirect reciprocity game. By introducing the notion of reputation and social norms, rational users who care about their future utility are incentivized to cooperate with others. Differently from existing works on reputation-based schemes that mainly rely on experimental verification, the effectiveness of the scheme is demonstrated in two steps. First, we conduct a steady-state analysis of the game and show that cooperating with users who have a good reputation can be sustained as an equilibrium when the cost-to-gain ratio is below a certain threshold. Then, by modeling the action spreading at transient states as an evolutionary game, we show that the equilibria we found in the steady-state analysis are stable and can be reached with proper initial conditions. Moreover, we introduce energy detection to handle the possible cheating behaviors of users and study its impact on the indirect reciprocity game.

Many spectrum sensing methods and dynamic access algorithms have been proposed to improve secondary users’ access opportunities. However, few of them have considered integrating the design of spectrum sensing and access algorithms together by taking into account the mutual influence between them. In this chapter, we focus on jointly analyzing the spectrum sensing and access problem. Due to their selfish nature, secondary users tend to act selfishly to access the channel without contributing to spectrum sensing. Moreover, they may employ out-of-equilibrium strategies because of the uncertainty of others’ strategies. To model the complicated interactions among secondary users, the joint spectrum sensing and access problem is formulated as an evolutionary game and the evolutionarily stable strategy (ESS) that no one will deviate from is studied. Furthermore, a distributed learning algorithm for the secondary users to converge to the ESS is introduced. Simulation results shows that the system can quickly converge to the ESS and such an ESS is robust to the sudden unfavorable deviations of the selfish secondary users.

Cooperation is a promising approach to simultaneously achieving efficient spectrum resource use and improving the quality of service of primary users in dynamic spectrum access networks. However, due to their selfish nature, how to stimulate secondary users to play cooperatively is an important issue. In this chapter, we discuss a reputation-based spectrum access framework where the cooperation stimulation problem is modeled as an indirect reciprocity game. In this game, secondary users choose how to help primary users relay information and gain reputation, based on which they can access a certain amount of vacant licensed channels in the future. By formulating a secondary user's decision-making as a Markov decision process, the optimal action rule can be obtained, according to which the secondary user will use maximal power to help the primary user relay data and thus greatly improve the primary user's quality of service as well as the spectrum utilization efficiency. Moreover, the uniqueness of the stationary reputation distribution is proved, and the conditions under which the optimal action rule is evolutionarily stable are theoretically derived.

Data sharing is a critical step in implementing data fusion, and how to encourage sensors to share their data is an important issue. In this chapter, we discuss a reputation-based incentive framework where the data-sharing stimulation problem is modeled as an indirect reciprocity game. In this game, sensors choose how to report their results to the fusion center and gain reputation, based on which they can obtain certain benefits in the future. Taking the sensing and fusion accuracy into account, reputation distribution is introduced into the game, where we prove theoretically the Nash equilibrium of the game and its uniqueness. Furthermore, we apply the scheme to cooperative spectrum sensing. We show that, within an appropriate cost-to-gain ratio, the optimal strategy for the secondary users is to report when the average received energy is above a given threshold and to keep silent otherwise. Such an optimal strategy is also proved to be a desirable evolutionarily stable strategy.