Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning-based Prediction, Rendering and Association Optimization for
  MEC-enabled Wireless Virtual Reality (VR) Network   Wireless-connected Virtual Reality (VR) provides immersive experience for VR
users from any-where at anytime. However, providing wireless VR users with
seamless connectivity and real-time VR video with high quality is challenging
due to its requirements in high Quality of Experience (QoE) and low VR
interaction latency under limited computation capability of VR device. To
address these issues,we propose a MEC-enabled wireless VR network, where the
field of view (FoV) of each VR user can be real-time predicted using Recurrent
Neural Network (RNN), and the rendering of VR content is moved from VR device
to MEC server with rendering model migration capability. Taking into account
the geographical and FoV request correlation, we propose centralized and
distributed decoupled Deep Reinforcement Learning (DRL) strategies to maximize
the long-term QoE of VR users under the VR interaction latency constraint.
Simulation results show that our proposed MEC rendering schemes and DRL
algorithms substantially improve the long-term QoE of VR users and reduce the
VR interaction latency compared to rendering at VR devices

21
The categories of this paper are: adap-org
3
The categories of this paper are: physics
6
The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas
9
The categories of this paper are: nlin
14
The categories of this paper are: math-ph
22
The categories of this paper are: chao-dyn
35
The categories of this paper are: mtrl-th
24
The categories of this paper are: dg-ga
31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int
20
The categories of this paper are: acc-phys
15
The categories of this paper are: nucl-ex
30
The categories of this paper are: q-alg
17
The categories of this paper are: q-fin
10
The categories of this paper are: q-bio