Learn to Optimize Resource Allocation under QoS Constraint of AR
This work addresses resource optimization for AR services, but it is incremental as it applies a known learning approach to a specific domain problem.
The paper tackles the problem of power allocation for interactive augmented reality services by modeling the transmission as a tandem queuing system and deriving a QoS bound, then uses a deep neural network to learn the allocation policy, resulting in reduced transmit power while meeting QoS requirements.
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.