# Learn to Optimize Resource Allocation under QoS Constraint of AR

arXiv: https://arxiv.org/abs/2501.16186 · HTML: https://mlsanity.com/paper/2501.16186/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 15 references that are in the map.
- First posted on arXiv 27 Jan 2025.

## Builds on (most cited first, 6 of 6)

- [Deep Models of Interactions Across Sets](https://mlsanity.com/paper/1803.02879/) (2018)
- [Resource Allocation of Federated Learning for the Metaverse with Mobile Augmented Reality](https://mlsanity.com/paper/2211.08705/) (2022)
- [Asynchronous Hybrid Reinforcement Learning for Latency and Reliability Optimization in the Metaverse over Wireless Communications](https://mlsanity.com/paper/2212.14749/) (2022)
- [Standardization of Extended Reality (XR) over 5G and 5G-Advanced 3GPP New Radio](https://mlsanity.com/paper/2203.02242/) (2022)
- [Prediction, Communication, and Computing Duration Optimization for VR Video Streaming](https://mlsanity.com/paper/1910.13884/) (2019)
- [AoI-Aware Resource Allocation for Smart Multi-QoS Provisioning](https://mlsanity.com/paper/2410.12384/) (2024)

## Abstract (arXiv)

This paper studies the uplink and downlink power allocation for interactive augmented reality (AR) services, where the live video captured by an AR device is uploaded to the network edge, and then the augmented video is subsequently downloaded. By modeling the AR transmission process as a tandem queuing system, we derive an upper bound for the probabilistic quality of service (QoS) requirement concerning end-to-end latency and reliability. The resource allocation under the QoS requirement results in a functional optimization problem. To address it, we design a deep neural network to learn the power allocation policy, leveraging the optimal power allocation structure to enhance learning performance. Simulation results demonstrate that the proposed method effectively reduces transmit power while meeting the QoS requirement.