This paper explores the difficulties of massive multi-user (MU) multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) detection with low-precision quantization. To solve these problems, we propose QMMO-Net, a novel deep unfolding (DU)-based detection scheme that fuses the architecture specialized for quantized MIMO-OFDM detection with data-driven techniques. To handle the severe distortions from coarse quantization, we add multiple trainable parameters to increase the model flexibility. With the help of the proposed differentiable proximal operator and DU tools, these parameters including a vector can be jointly optimized. Simulation results demonstrate that QMMO-Net outperforms traditional and DU-based detection algorithms in coarsely quantized MU-MIMO-OFDM systems. By combining the power of domain knowledge with data, our QMMO-Net has strong robustness to the non-linear effects of coarse quantization and the co-channel interference caused in high user load scenarios.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Deep Unfolding-based Detection for Quantized Massive MU-MIMO-OFDM Systems


    Contributors:


    Publication date :

    2022-06-01


    Size :

    2989099 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Hybrid Beamforming in mmWave MIMO-OFDM Systems via Deep Unfolding

    Chen, Kuan-Yuan / Chang, Hsin-Yuan / Chang, Ronald Y. et al. | IEEE | 2022


    Channel Estimation for FDD Massive MIMO OFDM Systems

    Hu, Die / He, Lianghua | IEEE | 2017



    Deep Unfolding for Fast Linear Massive MIMO Precoders under a PA Consumption Model

    Feys, Thomas / Mestre, Xavier / Peschiera, Emanuele et al. | IEEE | 2023


    Phase-Only OFDM Communication for Downlink Massive MIMO Systems

    Wiffen, Fred / Bocus, Mohammud Z. / Doufexi, Angela et al. | IEEE | 2018