2023/11/15 by Syed Asrar Ul Haq, haq, Syed Asrar ul, Varun Singh +5
Computer Science · Engineering · #Advanced Wireless Communication Techniques #FOS: Computer and information sciences #FOS: Electrical engineering #Full-Duplex Wireless Communications #Hardware Architecture (cs.AR) #Signal Processing (eess.SP) #Speech and Audio Processing #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2311.08689
openalex publication_date 2023/11/15 · openalex created_date 2023/11/17 · openalex updated_date 2026/07/28
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.