# Meta-Reinforcement Learning for Reliable Communication in THz/VLC Wireless VR Networks

arXiv: https://arxiv.org/abs/2102.12277 · HTML: https://mlsanity.com/paper/2102.12277/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 44 references that are in the map.
- First posted on arXiv 29 Jan 2021.

## Builds on (most cited first, 15 of 15)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [A Joint Learning and Communications Framework for Federated Learning over Wireless Networks](https://mlsanity.com/paper/1909.07972/) (2019)
- [Distributed Multi-agent Meta Learning for Trajectory Design in Wireless Drone Networks](https://mlsanity.com/paper/2012.03158/) (2020)
- [Policy Gradient Methods for Reinforcement Learning with Function Approximation and Action-Dependent Baselines](https://mlsanity.com/paper/1706.06643/) (2017)
- [Towards Low-Latency and Ultra-Reliable Virtual Reality](https://mlsanity.com/paper/1801.07587/) (2018)
- [Deep Learning for Optimal Deployment of UAVs with Visible Light Communications](https://mlsanity.com/paper/1912.00752/) (2019)
- [Cellular-Connected Wireless Virtual Reality: Requirements, Challenges, and Solutions](https://mlsanity.com/paper/2001.06287/) (2020)
- [A Machine Learning Approach for Task and Resource Allocation in Mobile Edge Computing Based Networks](https://mlsanity.com/paper/2007.10102/) (2020)
- [Combating the Distance Problem in the Millimeter Wave and Terahertz Frequency Bands](https://mlsanity.com/paper/1902.04682/) (2019)
- [Risk-Based Optimization of Virtual Reality over Terahertz Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2002.09052/) (2020)
- [Improving positioning accuracy of the mobile laser scanning in GPS-denied environments: An experimental case study](https://mlsanity.com/paper/1911.00041/) (2019)
- [Energy Efficient User Clustering, Hybrid Precoding and Power Optimization in Terahertz MIMO-NOMA Systems](https://mlsanity.com/paper/2005.01053/) (2020)
- [3-D Statistical Indoor Channel Model for Millimeter-Wave and Sub-Terahertz Bands](https://mlsanity.com/paper/2009.12971/) (2020)
- [Learning-based Prediction and Uplink Retransmission for Wireless Virtual Reality (VR) Network](https://mlsanity.com/paper/2012.12725/) (2020)

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

- [AI-driven Wireless Positioning: Fundamentals, Standards, State-of-the-art, and Challenges](https://mlsanity.com/paper/2501.14970/) (2025)
- [Optimizing Communication and Device Clustering for Clustered Federated Learning with Differential Privacy](https://mlsanity.com/paper/2507.07320/) (2025)
- [Hybrid-Task Meta-Learning: A GNN Approach for Scalable and Transferable Bandwidth Allocation](https://mlsanity.com/paper/2401.10253/) (2023)
- [Semantic Communication-Enabled Cloud-Edge-End-collaborative Metaverse Services Architecure](https://mlsanity.com/paper/2506.10001/) (2025)

## Abstract (arXiv)

In this paper, the problem of enhancing the quality of virtual reality (VR) services is studied for an indoor terahertz (THz)/visible light communication (VLC) wireless network. In the studied model, small base stations (SBSs) transmit high-quality VR images to VR users over THz bands and light-emitting diodes (LEDs) provide accurate indoor positioning services for them using VLC. Here, VR users move in real time and their movement patterns change over time according to their applications, where both THz and VLC links can be blocked by the bodies of VR users. To control the energy consumption of the studied THz/VLC wireless VR network, VLC access points (VAPs) must be selectively turned on so as to ensure accurate and extensive positioning for VR users. Based on the user positions, each SBS must generate corresponding VR images and establish THz links without body blockage to transmit the VR content. The problem is formulated as an optimization problem whose goal is to maximize the reliability of the VR network by selecting the appropriate VAPs to be turned on and controlling the user association with SBSs. To solve this problem, a policy gradient-based reinforcement learning (RL) algorithm that adopts a meta-learning approach is proposed. The proposed meta policy gradient (MPG) algorithm enables the trained policy to quickly adapt to new user movement patterns. In order to solve the problem of maximizing the average number of successfully served users for VR scenarios with a large number of users, a dual method based MPG algorithm (D-MPG) with a low complexity is proposed. Simulation results demonstrate that, compared to the trust region policy optimization algorithm (TRPO), the proposed MPG and D-MPG algorithms yield up to 26.8% and 21.9% improvement in the reliability as well as 81.2% and 87.5% gains in the convergence speed, respectively.