  In this paper, we propose a novel complex convolutional neural network (CNN)
CSI enhancer for integrated sensing and communications (ISAC), which exploits
the correlation between the sensing parameters (such as angle-of-arrival and
range) and the channel state information (CSI) to significantly improve the CSI
estimation accuracy and further enhance the sensing accuracy. Within the CNN
CSI enhancer, we use the complex-valued computation layers to form the CNN,
which maintains the phase information of CSI. We also transform the CSI into
the sparse angle-delay domain, leading to heatmap images with prominent peaks
that can be efficiently processed by CNN. Based on the enhanced CSI outputs, we
further propose a novel biased fast Fourier transform (FFT)-based sensing
scheme for improving the range sensing accuracy, by artificially introducing
phase biasing terms. Extensive simulation results show that the ISAC complex
CNN CSI enhancer can converge within 30 training epochs. The normalized mean
square error (NMSE) of its CSI estimates is about 17 dB lower than that of the
linear minimum mean square error (LMMSE) estimator, and the bit error rate
(BER) of demodulation using the enhanced CSI estimation approaches that with
perfect CSI. Finally, the range estimation MSE of the proposed biased FFT-based
sensing method approaches that of the subspace-based sensing method, at a much
lower complexity.
