Propagation Channel Modeling by Deep learning Techniques
This work addresses channel modeling for communication system design and optimization, representing an incremental improvement by applying existing deep learning techniques to a domain-specific problem.
The paper tackles the problem of modeling propagation channels in communication systems by treating time-frequency channel responses as images and using Deep Convolutional Generative Adversarial Networks (DCGANs) to model their distribution, with results showing significant statistical similarity to measurement data through a new 2D similarity metric.
Channel, as the medium for the propagation of electromagnetic waves, is one of the most important parts of a communication system. Being aware of how the channel affects the propagation waves is essential for designing, optimization and performance analysis of a communication system. For this purpose, a proper channel model is needed. This paper presents a novel propagation channel model which considers the time-frequency response of the channel as an image. It models the distribution of these channel images using Deep Convolutional Generative Adversarial Networks. Moreover, for the measurements with different user speeds, the user speed is considered as an auxiliary parameter for the model. StarGAN as an image-to-image translation technique is used to change the generated channel images with respect to the desired user speed. The performance of the proposed model is evaluated using existing metrics. Furthermore, to capture 2D similarity in both time and frequency, a new metric is introduced. Using this metric, the generated channels show significant statistical similarity to the measurement data.