Traditional communication system design has always been based on the paradigm of first establishing a mathematical model of the communication channel, then designing and optimizing the system according to the model. The advent of modern machine learning techniques, specifically deep neural networks, has opened up opportunities for data-driven system design and optimization. This article draws examples from the optimization of reconfigurable intelligent surface, distributed channel estimation and feedback for multiuser beamforming, and active sensing for millimeter wave (mmWave) initial alignment to illustrate that a data-driven design that bypasses explicit channel modelling can often discover excellent solutions to communication system design and optimization problems that are otherwise computationally difficult to solve. We show that by performing an end-to-end training of a deep neural network using a large number of channel samples, a machine learning based approach can potentially provide significant system-level improvements as compared to the traditional model-based approach for solving optimization problems. The key to the successful applications of machine learning techniques is in choosing the appropriate neural network architecture to match the underlying problem structure.
翻译:传统的通信系统设计一向以首先建立通信频道数学模型的模式为基础,然后根据模型设计和优化系统。现代机器学习技术的出现,特别是深神经网络的出现,为数据驱动系统设计和优化创造了机会。这一条从优化可重新配置的智能表面、为多用户波束进行分布式频道估计和反馈,以及对毫米波(毫米Wave)进行主动感测(mmWave)的初步调整中提取实例,以说明一个数据驱动的设计,绕过明确的频道建模,往往会发现通信系统设计和优化问题的出色解决方案,否则很难在计算上解决。我们表明,通过利用大量频道样本对深神经网络进行端到端培训,一种基于机器学习的方法可以提供与传统的基于模型的解决优化问题的方法相比的重大系统层面改进。成功应用机器学习技术的关键在于选择适当的神经网络结构,以适应潜在的问题结构。