  In this paper, the problem of enhancing the virtual reality (VR) experience
for wireless users is investigated by minimizing the occurrence of breaks in
presence (BIP) that can detach the users from their virtual world. To measure
the BIP for wireless VR users, a novel model that jointly considers the VR
application type, transmission delay, VR video quality, and users' awareness of
the virtual environment is proposed. In the developed model, the base stations
(BSs) transmit VR videos to the wireless VR users using directional
transmission 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 user
may result in a blockage of its wireless link, the location and orientation of
VR users must also be considered when minimizing BIP. The BIP minimization
problem is formulated as an optimization problem which jointly considers the
predictions of users' locations, orientations, and their BS association. To
predict the orientation and locations of VR users, a distributed learning
algorithm based on the machine learning framework of deep (ESNs) is proposed.
The proposed algorithm uses concept from federated learning to enable multiple
BSs to locally train their deep ESNs using their collected data and
cooperatively build a learning model to predict the entire users' locations and
orientations. Using these predictions, the user association policy that
minimizes BIP is derived. Simulation results demonstrate that the developed
algorithm reduces the users' BIP by up to 16% and 26%, respectively, compared
to centralized ESN and deep learning algorithms.
