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, 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 echo state networks (ESNs) is proposed. The proposed algorithm uses 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.

中文翻译：

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用于最小化无线虚拟现实网络中存在中断的联合回声状态学习

在本文中，通过最大限度地减少可以将用户与其虚拟世界分离的中断在场 (BIP) 的发生来研究增强无线用户的虚拟现实 (VR) 体验的问题。为了测量无线VR用户的BIP，提出了一种综合考虑VR应用类型、传输延迟、VR视频质量和用户对虚拟环境感知的新模型。在开发的模型中，基站（BS）使用定向传输链路向无线 VR 用户传输 VR 视频，为 VR 用户提供高数据速率，从而减少每个用户的 BIP 数量。由于 VR 用户的身体运动可能会导致其无线链路的阻塞，因此在最小化 BIP 时还必须考虑 VR 用户的位置和方向。BIP 最小化问题被表述为一个优化问题，它联合考虑了用户位置、方向及其与 BS 关联的预测。为了预测 VR 用户的方向和位置，提出了一种基于深度回声状态网络 (ESN) 机器学习框架的分布式学习算法。所提出的算法使用联邦学习使多个 BS 能够使用他们收集的数据在本地训练他们的深度 ESN，并合作构建一个学习模型来预测整个用户的位置和方向。使用这些预测，可以导出最小化 BIP 的用户关联策略。仿真结果表明，与集中式 ESN 和深度学习算法相比，所开发的算法分别将用户的 BIP 降低了 16% 和 26%。