Millimeter Wave Massive MIMO Heterogeneous Networks Using Fuzzy-Based Deep Convolutional Neural Network (FDCNN)

Enabling high mobility applications in millimeter wave (mmWave) based systems opens up a slew of new possibilities, including vehicle communi-cations in addition to wireless virtual/augmented reality. The narrow beam usage in addition to the millimeter waves sensitivity might block the coverage along with the reliability of the mobile links. In this research work, the improvement in the quality of experience faced by the user for multimedia-related applications over the millimeter-wave band is investigated. The high attenuation loss in high frequencies is compensated with a massive array structure named Multiple Input and Multiple Output (MIMO) which is utilized in a hyperdense environment called heterogeneous networks (HetNet). The optimization problem which arises while maximizing the Mean Opinion Score (MOS) is analyzed along with the QoE(Quality of Experience) metric by considering the Base Station(BS) powers in addition to the needed Quality of Service (QoS). Most of the approaches related to wireless network communication are not suitable for the millimeter-wave band because of its problems due to high complexity and its dynamic nature. Hence a deep reinforcement learning framework is developed for tackling the same opti-mization problem. In this work, a Fuzzy-based Deep Convolutional Neural Net-work (FDCNN) is proposed in addition to a Deep Reinforcing Learning Framework (DRLF) for extracting the features of highly correlated data. The investigational results prove that the proposed method yields the highest satisfac-tion to the user by increasing the number of antennas in addition with the small-scale antennas at the base stations. The proposed work outperforms in terms of MOS with multiple antennas.