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Channel estimation is crucial for modern WiFi system and becomes more andmore challenging with the growth of user throughput in multiple input multipleoutput configuration. Plenty of literature spends great efforts in improvingthe estimation accuracy, while the interpolation schemes are overlooked. Todeal with this challenge, we exploit the super-resolution image recovery schemeto model the non-linear interpolation mechanisms without pre-assumed channelcharacteristics in this paper. To make it more practical, we offline generatenumerical channel coefficients according to the statistical channel models totrain the neural networks, and directly apply them in some practical WiFiprototype systems. As shown in this paper, the proposed super-resolution basedchannel estimation scheme can outperform the conventional approaches in bothLOS and NLOS scenarios, which we believe can significantly change the currentchannel estimation method in the near future.

Channel Estimation for WiFi Prototype Systems with Super-Resolution Image Recovery