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The channel estimation (CE) in wireless receivers is one of the most criticaland computationally complex signal processing operations. Recently, variousworks have shown that the deep learning (DL) based CE outperforms conventionalminimum mean square error (MMSE) based CE, and it is hardware-friendly.However, DL-based CE has higher complexity and latency than popularly usedleast square (LS) based CE. In this work, we propose a novel low complexityhigh-speed Deep Neural Network-Augmented Least Square (LC-LSDNN) algorithm forIEEE 802.11p wireless physical layer and efficiently implement it on Zynqsystem on chip (ZSoC). The novelty of the LC-LSDNN is to use different DNNs forreal and imaginary values of received complex symbols. This helps reduce thesize of DL by 59% and optimize the critical path, allowing it to operate at 60%higher clock frequency. We also explore three different architectures forMMSE-based CE. We show that LC-LSDNN significantly outperforms MMSE andstate-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, witharound 50% lower resources than existing DL-based CE.

Low Complexity High Speed Deep Neural Network Augmented Wireless Channel Estimation