Low Complexity High Speed Deep Neural Network Augmented Wireless Channel Estimation

TL;DR

This paper introduces a low complexity, high-speed deep neural network-augmented least squares algorithm for wireless channel estimation, achieving better performance and efficiency on Zynq hardware compared to existing methods.

Contribution

It proposes a novel DNN architecture that reduces complexity and increases speed for wireless channel estimation, with optimized implementation on Zynq SoC.

Findings

Outperforms MMSE and state-of-the-art DL-based CE across SNRs and channels

Reduces DNN size by 59% and increases clock frequency by 60%

Uses 50% fewer resources than existing DL-based CE methods

Abstract

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.