# Feeling of Presence Maximization: mmWave-Enabled Virtual Reality Meets Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2107.01001 · HTML: https://mlsanity.com/paper/2107.01001/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 55 references that are in the map.
- First posted on arXiv 3 Jun 2021.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Scheduling Policies for Federated Learning in Wireless Networks](https://mlsanity.com/paper/1908.06287/) (2019)
- [Viewport-Adaptive Navigable 360-Degree Video Delivery](https://mlsanity.com/paper/1609.08042/) (2016)
- [Power-Efficient Wireless Streaming of Multi-Quality Tiled 360 VR Video in MIMO-OFDMA Systems](https://mlsanity.com/paper/2105.09865/) (2021)
- [Probabilistic Tile Visibility-Based Server-Side Rate Adaptation for Adaptive 360-Degree Video Streaming](https://mlsanity.com/paper/1906.08575/) (2019)
- [Multicast eMBB and Bursty URLLC Service Multiplexing in a CoMP-Enabled RAN](https://mlsanity.com/paper/2002.09194/) (2020)
- [QoE-Driven UAV-Enabled Pseudo-Analog Wireless Video Broadcast: A Joint Optimization of Power and Trajectory](https://mlsanity.com/paper/2006.14438/) (2020)
- [Inter-Operator Resource Management for Millimeter Wave, Multi-Hop Backhaul Networks](https://mlsanity.com/paper/1704.03620/) (2017)
- [Edge Computing Meets Millimeter-wave Enabled VR: Paving the Way to Cutting the Cord](https://mlsanity.com/paper/1801.07614/) (2018)
- [How Should I Orchestrate Resources of My Slices for Bursty URLLC Service Provision?](https://mlsanity.com/paper/1912.00579/) (2019)

## Built on by (most cited first, 4 of 4)

- [Attention-aware Resource Allocation and QoE Analysis for Metaverse xURLLC Services](https://mlsanity.com/paper/2208.05438/) (2022)
- [Federated Multi-View Synthesizing for Metaverse](https://mlsanity.com/paper/2401.00859/) (2023)
- [Knowledge and Data Dual-Driven Channel Estimation and Feedback for Ultra-Massive MIMO Systems under Hybrid Field Beam Squint Effect](https://mlsanity.com/paper/2403.12813/) (2024)
- [User-centric Heterogeneous-action Deep Reinforcement Learning for Virtual Reality in the Metaverse over Wireless Networks](https://mlsanity.com/paper/2302.01471/) (2023)

## Abstract (arXiv)

This paper investigates the problem of providing ultra-reliable and energy-efficient virtual reality (VR) experiences for wireless mobile users. To ensure reliable ultra-high-definition (UHD) video frame delivery to mobile users and enhance their immersive visual experiences, a coordinated multipoint (CoMP) transmission technique and millimeter wave (mmWave) communications are exploited. Owing to user movement and time-varying wireless channels, the wireless VR experience enhancement problem is formulated as a sequence-dependent and mixed-integer problem with a goal of maximizing users' feeling of presence (FoP) in the virtual world, subject to power consumption constraints on access points (APs) and users' head-mounted displays (HMDs). The problem, however, is hard to be directly solved due to the lack of users' accurate tracking information and the sequence-dependent and mixed-integer characteristics. To overcome this challenge, we develop a parallel echo state network (ESN) learning method to predict users' tracking information by training fresh and historical tracking samples separately collected by APs. With the learnt results, we propose a deep reinforcement learning (DRL) based optimization algorithm to solve the formulated problem. In this algorithm, we implement deep neural networks (DNNs) as a scalable solution to produce integer decision variables and solving a continuous power control problem to criticize the integer decision variables. Finally, the performance of the proposed algorithm is compared with various benchmark algorithms, and the impact of different design parameters is also discussed. Simulation results demonstrate that the proposed algorithm is more 4.14% energy-efficient than the benchmark algorithms.