Coordinated beamforming for hyper-cellular mmWave communications using machine learning

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Κωνσταντόπουλος, Γεώργιος

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The rapid evolution of technology, the increasing use of wireless devices and the ever-increasing volume of data that needs to be transferred have created the need to design new, innovative standards for the fast and reliable distribution of information. The new generation of 5G wireless telecommunications is set to provide a solution to this problem. The 5G generation promises a big increase in data transmission speeds, as well as global coverage through the interconnection of all devices in a network and a large increase in network coverage stations. It is understood that managing such a large network is a complex process and requires large amounts of energy to achieve. Advances in machine learning are creating new perspectives for the design of fifth generation telecommunication systems and for the optimization of automated data management techniques. The purpose of this paper is to present an innovative concept in which machine learning techniques can be used to select the antenna stations that will serve each user and how they will be served, in a mmWave coordinated beamforming scenario. The consequence of this technique is a drastic reduction of the energy footprint of the network, through the temporary deactivation of antenna stations not selected to serve users. It has to be noted that for our simulations we will use the system model and the data of the DeepMIMO project. The present work contains six chapters. Chapter 1 provides a detailed description of the fifth generation systems and the technologies they use. Chapter 2 discusses hyper-cellular networks which is a specific type of network we will deal with and gives basic concepts and information about it. Chapter 3 gives an extensive description of the subject of machine learning and in particular neural networks and ways of training them. Chapter 4 describes the implementation of the DeepMIMO system model and discusses in detail the concept of coordinated beamforming. In Chapter 5 there is the final implementation of the system and its simulation results and in Chapter 6 the main conclusions of the paper are collected and suggestions for further research of the system are made.



Coordinated beamforming, Cellular networks, Machine learning