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In this paper, the problem of enhancing the virtual reality (VR) experiencefor wireless users is investigated by minimizing the occurrence of breaks inpresence (BIP) that can detach the users from their virtual world. To measurethe BIP for wireless VR users, a novel model that jointly considers the VRapplication type, transmission delay, VR video quality, and users' awareness ofthe virtual environment is proposed. In the developed model, the base stations(BSs) transmit VR videos to the wireless VR users using directionaltransmission links so as to provide high data rates for the VR users, thus,reducing the number of BIP for each user. Since the body movements of a VR usermay result in a blockage of its wireless link, the location and orientation ofVR users must also be considered when minimizing BIP. The BIP minimizationproblem is formulated as an optimization problem which jointly considers thepredictions of users' locations, orientations, and their BS association. Topredict the orientation and locations of VR users, a distributed learningalgorithm based on the machine learning framework of deep (ESNs) is proposed.The proposed algorithm uses concept from federated learning to enable multipleBSs to locally train their deep ESNs using their collected data andcooperatively build a learning model to predict the entire users' locations andorientations. Using these predictions, the user association policy thatminimizes BIP is derived. Simulation results demonstrate that the developedalgorithm reduces the users' BIP by up to 16% and 26%, respectively, comparedto centralized ESN and deep learning algorithms.

Federated Echo State Learning for Minimizing Breaks in Presence in Wireless Virtual Reality Networks