Learn to Optimize Resource Allocation under QoS Constraint of AR

• URL: http://arxiv.org/abs/2501.16186v1
• Date: Mon, 27 Jan 2025 16:42:48 GMT
• Title: Learn to Optimize Resource Allocation under QoS Constraint of AR
• Authors: Shiyong Chen, Yuwei Dai, Shengqian Han,
• Abstract summary: We study the uplink and downlink power allocation for interactive augmented reality (AR) services, where live video is uploaded to the network edge and then the augmented video is 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. We propose a deep neural network to learn the power allocation policy, leveraging the structure of optimal power allocation to enhance learning performance.
• Score: 6.073675653083644
• License:
• Abstract: This paper studies the uplink and downlink power allocation for interactive augmented reality (AR) services, where 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 with the QoS constraints results in a functional optimization problem. To address it, we design a deep neural network to learn the power allocation policy, leveraging the structure of optimal power allocation to enhance learning performance. Simulation results demonstrate that the proposed method effectively reduces transmit powers while meeting the QoS requirement.

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