基于深度学习的质量驱动无线视频传输功率控制
Deep Learning Based Power Control for Quality-Driven Wireless Video Transmissions
摘要：
本文研究了在总传输功率和最小视频质量约束下的多用户无线视频传输问题。为了提供所需的性能水平的最终用户在实时视频传输，同时有效地使用能源，我们假设，功率控制。由于干扰的存在，确定最优功率控制是一个非凸问题，但可以通过单调优化框架来解决。然而，单调优化是一种迭代算法，并且通常会带来相当大的计算复杂性，使其不适合于实时应用。为了解决这个问题，我们提出了一种基于学习的方法，将资源分配算法的输入和输出作为一个未知的非线性映射，并采用深度神经网络（DNN）来学习这个映射。这种经由DNN学习的映射可以针对给定的信道条件快速提供最佳功率电平。
英文摘要：
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.
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