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End users' quality of experience (QoE) is one of the most crucial requirements to be considered in device-to-device communication. Users' QoE is affected by the ratio of the amount of received data to the amount of shared data. The ratio should be approximate to 1. To achieve this, we propose an evolutionary algorithm-based cooperative content caching and communication (EACCC) scheme. This scheme works by finding the maximum 1-factor of a directed weighted graph, where each edge's weight corresponds to the mean opinion score of the connection between two users. Finding the maximum 1-factor of graph-based communication is a problem that is difficult to solve optimally, but an evolutionary algorithm can quickly converge to a high-quality solution. Each user provides an opinion score of incoming and outgoing connection, and the maximum 1-factor corresponds to the scheme that maximizes overall QoE. The proposed EACCC scheme achieves optimal connections among users in a vast and complex network, due to strong mutual communication. The simulation results verify that our proposed scheme outperforms traditional QoE optimization schemes. Moreover, our scheme can be implemented easily in a realistic scenario.

Maximizing Quality of Experience in Device-to-Device Communication Using an Evolutionary Algorithm Based on Users’ Behavior