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Meta-Reinforcement Learning for Reliable Communication in THz/VLC Wireless VR Networks
In this paper, the problem of enhancing the quality of virtual reality (...
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Echo-Liquid State Deep Learning for 360^∘ Content Transmission and Caching in Wireless VR Networks with Cellular-Connected UAVs
In this paper, the problem of content caching and transmission is studie...
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Predictive Scheduling for Virtual Reality
A significant challenge for future virtual reality (VR) applications is ...
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Echo State Transfer Learning for Data Correlation Aware Resource Allocation in Wireless Virtual Reality
In this paper, the problem of data correlation-aware resource management...
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Taming the latency in multi-user VR 360^∘: A QoE-aware deep learning-aided multicast framework
Immersive virtual reality (VR) applications are known to require ultra-h...
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Visible Light Communication for Next Generation Untethered Virtual Reality Systems
Virtual and augmented reality (VR/AR) systems are emerging technologies ...
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Human-in-the-Loop Wireless Communications: Machine Learning and Brain-Aware Resource Management
Human-centric applications such as virtual reality and immersive gaming ...
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Federated Echo State Learning for Minimizing Breaks in Presence in Wireless Virtual Reality Networks
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 (BIPs) that can detach the users from their virtual world. To measure the BIPs for wireless VR users, a novel model that jointly considers the VR applications, 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 increase the data rate of VR users, thus, reducing the number of BIPs for each user. Therefore, the mobility and orientation of VR users must be considered when minimizing BIPs, since the body movements of a VR user may result in blockage of its wireless link. The BIP problem is formulated as an optimization problem which jointly considers the predictions of users' mobility patterns, orientations, and their BS association. To predict the orientation and mobility patterns of VR users, a distributed learning algorithm based on the machine learning framework of deep echo state networks (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' mobility patterns and orientations. Using these predictions, the user association policy that minimizes BIPs is derived. Simulation results demonstrate that the developed algorithm reduces the users' BIPs by up to 16 respectively, compared to centralized ESN and deep learning algorithms.
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