论文标题
使用元学习和生成对抗网络的深度学习辅助无线系统的大量数据生成
Massive Data Generation for Deep Learning-aided Wireless Systems Using Meta Learning and Generative Adversarial Network
论文作者
论文摘要
作为设计通信系统的全新范式，深度学习（DL）是机器学习所需的无线功能的一种方法，最近引起了很多关注。为了充分实现DL辅助无线系统的好处，我们需要收集大量培训样本。不幸的是，在实际环境中收集大量样品非常具有挑战性，因为它需要大量的信号传输开销。在本文中，我们为DL辅助无线系统提出了一种新型的数据采集框架。在我们的工作中，生成对抗网络（GAN）用于生成近似真实样品的样品。为了减少无线数据生成所需的培训样本量，我们在元学习的帮助下训练GAN。从数值实验中，我们表明，由GAN生成的样品训练的DL模型的性能接近由真实样品训练的DL模型。
As an entirely-new paradigm to design the communication systems, deep learning (DL), an approach that the machine learns the desired wireless function, has received much attention recently. In order to fully realize the benefit of DL-aided wireless system, we need to collect a large number of training samples. Unfortunately, collecting massive samples in the real environments is very challenging since it requires significant signal transmission overhead. In this paper, we propose a new type of data acquisition framework for DL-aided wireless systems. In our work, generative adversarial network (GAN) is used to generate samples approximating the real samples. To reduce the amount of training samples required for the wireless data generation, we train GAN with the help of the meta learning. From numerical experiments, we show that the DL model trained by the GAN generated samples performs close to that trained by the real samples.
