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arXiv 1808.01672cs.ITeess.SPmath.IT
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模型辅助无线人工智能:在深度神经网络中嵌入专家知识以实现无线系统优化

Model-Aided Wireless Artificial Intelligence: Embedding Expert Knowledge in Deep Neural Networks Towards Wireless Systems Optimization

Alessio Zappone, Marco Di Renzo, Mérouane Debbah, Thanh Tu Lam, Xuewen Qian

摘要:

基于人工神经网络的深度学习是一种强大的机器学习方法,在过去几年中,该方法已成功用于实现任务,例如,图像分类、语音识别、语言翻译等,这些通常很容易由人类执行,但很难由机器执行。这就是为什么深度学习被认为是实现人工智能概念的主要推动力之一的原因之一。为了识别允许拟合输入-输出数据对的人工神经网络的最佳架构,深度学习方法中的当前方法包括采用数据驱动的方法。一旦人工神经网络被训练,它就能够通过基于过去获得的知识提供最佳输出来响应从未观察到的输入。在这种背景下,深度学习社区的最新趋势是用基于专家知识的先验信息来补充纯数据驱动的方法。在这项工作中,我们描述了两种方法,实现这一战略,旨在优化无线通信网络。此外,我们说明数值结果,以评估所提出的方法相比,纯数据驱动的实现的性能。

英文摘要:

Deep learning based on artificial neural networks is a powerful machine learning method that, in the last few years, has been successfully used to realize tasks, e.g., image classification, speech recognition, translation of languages, etc., that are usually simple to execute by human beings but extremely difficult to perform by machines. This is one of the reasons why deep learning is considered to be one of the main enablers to realize the notion of artificial intelligence. In order to identify the best architecture of an artificial neural network that allows one to fit input-output data pairs, the current methodology in deep learning methods consists of employing a data-driven approach. Once the artificial neural network is trained, it is capable of responding to never-observed inputs by providing the optimum output based on past acquired knowledge. In this context, a recent trend in the deep learning community is to complement pure data-driven approaches with prior information based on expert knowledge. In this work, we describe two methods that implement this strategy, which aim at optimizing wireless communication networks. In addition, we illustrate numerical results in order to assess the performance of the proposed approaches compared with pure data-driven implementations.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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