Channel estimation and signal detection with model-driven deep learning for massive multiuser MIMO-OFDM systems
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Date
19/07/2023Item status
Restricted AccessEmbargo end date
19/07/2024Author
Liu, Changjiang
Metadata
Abstract
The emerging fifth generation (5G) and beyond wireless systems raise requirements
on improved coverage, lower latency, higher data rates, and energy efficiency. These
improvements can be provided by massive Multiple-Input Multiple-Output (MIMO),
which is one of the essential technologies of 5G. However, due to a large number of
antennas and radio frequency chains, the receiver design for massive MIMO systems
becomes more challenging. Moreover, non-linear effects in communication systems
like hardware impairment are hard to be modeled. Since conventional solutions
struggle to address these challenges, deep learning (DL) is considered as a promising
approach for the sixth generation cellular systems. The application of DL in the
physical layer is still in a nascent stage. Most prior DL-based receivers are purely
data-driven and aim mainly for performance improvement. By contrast, model-driven
DL-based massive MIMO receivers can potentially achieve lower complexity, better
interpretability and robustness by introducing expert knowledge, but this is not
yet well-investigated in the literature. This PhD thesis focuses on designing lowcomplexity
channel estimation and signal detection schemes with the application
of model-driven DL techniques for massive MIMO orthogonal frequency-division
multiplexing (OFDM) systems.
Firstly, a novel model-aided DL-based OFDM receiver, which integrates a convolutional
neural network (CNN)-based channel estimator and a signal detection neural
network (SD-NN), is proposed. By exploiting channel correlations in the time and
frequency domain, the proposed CNN-based scheme can efficiently denoise and
refine the image-like low-resolution channel with time-variant and frequency-selective
effects. Then, the explicit channel information from the CNN is processed by a modelbased
equalizer, and the output is used as a proper initialization for SD-NN. Moreover,
several data preprocessing and model tuning strategies are developed to improve
detection performance. As a result, unlike the data-driven solutions viewing the whole
receiver as a black box, the proposed model-aided DL architecture achieves lower
complexity and faster convergence with only a small number of pilots.
Secondly, considering the complexity problem of generic NN architectures in massive
MIMO-OFDM systems, an efficient deep unfolding (DU)-based detection network is
developed. Based on the domain knowledge of MIMO detection, the alternating direction
method of multipliers (ADMM)-based network skeleton is first derived. To get a
specialized architecture with improved model flexibility for DU-based MIMO-OFDM
detection, the over-relaxation parameter and an additional step size are added to the
network skeleton.With the help of DU techniques, the sets of trainable parameters can
be optimized explicitly for different subcarriers to enhance the detection performance
under realistic channels with severe spatial and frequency correlations. Furthermore,
a differentiable projection function is designed to enable learning-based parameter
optimization. Compared to existing baselines in the literature, the proposed approach
can provide a better performance-complexity trade-off, especially for the cases of high
user load and real-world correlated channels.
Thirdly, for high-loaded massive MIMO systems with large numbers of users, a
frequency-orthogonal pilot scheme is designed to save time resources used for pilot
transmission. By a special subtractive residual layer, the proposed denoising CNN
learns the denoising mapping to eliminate channel noise in the delay domain instead
of learning the labeled channel matrices directly. To further improve estimation
performance, a residual CNN is proposed to exploit spatial-frequency correlations of
channels and refine the output of the denoising CNN. With the help of a customized
fast Fourier transform layer, these two CNNs can be jointly trained across different
domains, resulting in an end-to-end channel estimation network. This dual CNN-based
estimator is shown to achieve state-of-the-art performance and fast convergence with
lower complexity than baseline approaches.
Finally, the research is extended to massive MIMO-OFDM systems with low-precision
analog-to-digital converters (ADCs). To accurately detect the received signals with
severe non-linear distortions under such complex systems, a novel model-driven
detection network is proposed. Since well-established architectures like ADMM can
not handle errors of coarse quantization, a flexible non-linear estimator is first derived
to replace the general x-update of ADMM. Specifically, the scalar step size is upgraded
to a learnable vector used as the multiplicative gradient correction, and an additive
gradient correction is also added. Correspondingly, a specialized network skeleton with
multiple trainable parameters and an adaptive proximal operator is designed. By fusing
the model-based architecture and data-driven techniques, the proposed scheme shows
superior detection performance and robustness in coarsely quantized massive multiuser
MIMO-OFDM systems.
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