Energy-Aware Dynamic Cooperative Strategy Selection for Relay-Assisted Cellular Networks: An Evolutionary Game Approach

In relay-assisted cellular networks, when and how to cooperate for potential relay nodes is an important issue, particularly from the energy-aware perspective. Moreover, addressing the dynamic cooperative partner-selection problem with incomplete private information is a challenging issue, particularly in the competitive and changing environments. In this paper, we model the energy-aware dynamic competition of cooperative partner selection as an evolutionary game. Specifically, the replicator dynamics are applied to model the adaptation of strategic interactions while considering the dynamic nature in time dependence, and the evolutionarily stable strategy (ESS) is referred to as the robust solution of the proposed game. Moreover, we prove the uniqueness of the resulting ESS, guarantee the convergence to the ESS, and analyze the convergence behavior under the replicator dynamics from different perspectives. In practical terms, we further develop a cooperative partner-selection evolutionary algorithm based on the Q-learning approach, which only requires local knowledge for the potential relay nodes. In addition, we extend our observation by studying the dynamic cooperative partner selection with delay and discuss the properties of the resulting ESS.