  The channel estimation (CE) in wireless receivers is one of the most critical
and computationally complex signal processing operations. Recently, various
works have shown that the deep learning (DL) based CE outperforms conventional
minimum mean square error (MMSE) based CE, and it is hardware-friendly.
However, DL-based CE has higher complexity and latency than popularly used
least square (LS) based CE. In this work, we propose a novel low complexity
high-speed Deep Neural Network-Augmented Least Square (LC-LSDNN) algorithm for
IEEE 802.11p wireless physical layer and efficiently implement it on Zynq
system on chip (ZSoC). The novelty of the LC-LSDNN is to use different DNNs for
real and imaginary values of received complex symbols. This helps reduce the
size of DL by 59% and optimize the critical path, allowing it to operate at 60%
higher clock frequency. We also explore three different architectures for
MMSE-based CE. We show that LC-LSDNN significantly outperforms MMSE and
state-of-the-art DL-based CE for a wide range of signal-to-noise ratios (SNR)
and different wireless channels. Also, it is computationally efficient, with
around 50% lower resources than existing DL-based CE.
