  Deep learning has shown promise in enhancing channel state information (CSI)
feedback. However, many studies indicate that better feedback performance often
accompanies higher computational complexity. Pursuing better
performance-complexity tradeoffs is crucial to facilitate practical deployment,
especially on computation-limited devices, which may have to use lightweight
autoencoder with unfavorable performance. To achieve this goal, this paper
introduces knowledge distillation (KD) to achieve better tradeoffs, where
knowledge from a complicated teacher autoencoder is transferred to a
lightweight student autoencoder for performance improvement. Specifically, two
methods are proposed for implementation. Firstly, an autoencoder KD-based
method is introduced by training a student autoencoder to mimic the
reconstructed CSI of a pretrained teacher autoencoder. Secondly, an encoder
KD-based method is proposed to reduce training overhead by performing KD only
on the student encoder. Additionally, a variant of encoder KD is introduced to
protect user equipment and base station vendor intellectual property. Numerical
simulations demonstrate that the proposed methods can significantly improve the
student autoencoder's performance, while reducing the number of floating point
operations and inference time to 3.05%-5.28% and 13.80%-14.76% of the teacher
network, respectively. Furthermore, the variant encoder KD method effectively
enhances the student autoencoder's generalization capability across different
scenarios, environments, and bandwidths.
