Channel Estimation for One-Bit Multiuser Massive MIMO Using Conditional GAN

Channel estimation is a challenging task, especially in a massive multiple-input multiple-output (MIMO) system with one-bit analog-to-digital converters (ADC). Traditional deep learning (DL) methods, that learn the mapping from inputs to real channels, have significant difficulties in estimating accurate channels because their loss functions are not well designed and investigated. In this paper, a conditional generative adversarial networks (cGAN) is developed to predict more realistic channels by adversarially training two DL networks. cGANs not only learn the mapping from quantized observations to real channels but also learn an adaptive loss function to correctly train the networks. Numerical results show that the proposed cGAN based approach outperforms existing DL methods and achieves high robustness in massive MIMO systems.

[1]  Amine Mezghani,et al.  Channel Estimation in One-Bit Massive MIMO Systems: Angular Versus Unstructured Models , 2019, IEEE Journal of Selected Topics in Signal Processing.

[2]  Robert W. Heath,et al.  Channel estimation in millimeter wave MIMO systems with one-bit quantization , 2014, 2014 48th Asilomar Conference on Signals, Systems and Computers.

[3]  Jae-Mo Kang,et al.  Deep Learning-Based Channel Estimation for Massive MIMO Systems , 2019, IEEE Wireless Communications Letters.

[4]  Geoffrey Ye Li,et al.  Deep Learning-Based Channel Estimation for Beamspace mmWave Massive MIMO Systems , 2018, IEEE Wireless Communications Letters.

[5]  Geoffrey Ye Li,et al.  Deep Learning-Based Downlink Channel Prediction for FDD Massive MIMO System , 2019, IEEE Communications Letters.

[6]  Brian L. Evans,et al.  Robust Learning-Based ML Detection for Massive MIMO Systems with One-Bit Quantized Signals , 2018, 2019 IEEE Global Communications Conference (GLOBECOM).

[7]  Alexei A. Efros,et al.  Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[9]  Robert W. Heath,et al.  Near Maximum-Likelihood Detector and Channel Estimator for Uplink Multiuser Massive MIMO Systems With One-Bit ADCs , 2015, IEEE Transactions on Communications.

[10]  Ahmed Alkhateeb,et al.  DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications , 2019, ArXiv.

[11]  Guan Gui,et al.  Deep Learning for Super-Resolution Channel Estimation and DOA Estimation Based Massive MIMO System , 2018, IEEE Transactions on Vehicular Technology.

[12]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[13]  Andrea Vedaldi,et al.  Instance Normalization: The Missing Ingredient for Fast Stylization , 2016, ArXiv.

[14]  Philip Schniter,et al.  Expectation-Maximization Gaussian-Mixture Approximate Message Passing , 2012, IEEE Transactions on Signal Processing.

[15]  Nicolas Macris,et al.  Entropy and mutual information in models of deep neural networks , 2018, NeurIPS.

[16]  Shi Jin,et al.  Channel Estimation for Millimeter Wave Massive MIMO Systems with Low-Resolution ADCs , 2019, 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).

[17]  Geoffrey Ye Li,et al.  Deep Learning Based Channel Estimation for Massive MIMO With Mixed-Resolution ADCs , 2019, IEEE Communications Letters.

[18]  Sven Jacobsson,et al.  One-bit massive MIMO: Channel estimation and high-order modulations , 2015, 2015 IEEE International Conference on Communication Workshop (ICCW).

[19]  Yu Zhang,et al.  Deep Learning for Massive MIMO With 1-Bit ADCs: When More Antennas Need Fewer Pilots , 2020, IEEE Wireless Communications Letters.

[20]  Cheng Tao,et al.  Channel Estimation and Performance Analysis of One-Bit Massive MIMO Systems , 2016, IEEE Transactions on Signal Processing.

[21]  Hamid Sheikhzadeh,et al.  Deep Learning-Based Channel Estimation , 2018, IEEE Communications Letters.

[22]  Sebastian Ruder,et al.  An overview of gradient descent optimization algorithms , 2016, Vestnik komp'iuternykh i informatsionnykh tekhnologii.

[23]  Simon Osindero,et al.  Conditional Generative Adversarial Nets , 2014, ArXiv.

[24]  Erik G. Larsson,et al.  Massive MIMO for next generation wireless systems , 2013, IEEE Communications Magazine.