Deep Learning Based Power Control for Quality-Driven Wireless Video Transmissions
This work addresses the challenge of efficient real-time video transmission in wireless networks, offering a practical solution for improving energy use and quality of service, though it is incremental as it applies an existing deep learning approach to a known bottleneck.
The paper tackles the problem of real-time wireless video transmission to multiple users under power and quality constraints by proposing a deep learning-based power control method to replace computationally intensive monotonic optimization, achieving fast optimal power allocation for given channel conditions.
In this paper, wireless video transmission to multiple users under total transmission power and minimum required video quality constraints is studied. In order to provide the desired performance levels to the end-users in real-time video transmissions while using the energy resources efficiently, we assume that power control is employed. Due to the presence of interference, determining the optimal power control is a non-convex problem but can be solved via monotonic optimization framework. However, monotonic optimization is an iterative algorithm and can often entail considerable computational complexity, making it not suitable for real-time applications. To address this, we propose a learning-based approach that treats the input and output of a resource allocation algorithm as an unknown nonlinear mapping and a deep neural network (DNN) is employed to learn this mapping. This learned mapping via DNN can provide the optimal power level quickly for given channel conditions.