Deep-Learning Based Linear Precoding for MIMO Channels with Finite-Alphabet Signaling.
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Deep-Learning Based Linear Precoding for MIMO Channels with Finite-Alphabet Signaling
Introduction to MIMO Channels and Finite-Alphabet Signaling
Multiple-input multiple-output (MIMO) communication systems are a cornerstone of modern wireless communication, enabling higher data rates and improved reliability. However, the use of finite-alphabet signaling, such as QAM or PSK, introduces significant challenges in designing efficient linear precoders due to the complexity of optimizing the constellation-constrained mutual information .
Challenges in Traditional Precoding Methods
Traditional methods for linear precoding in MIMO systems often involve iterative optimization techniques, which are computationally intensive and not suitable for real-time applications. These methods typically require solving complex optimization problems to maximize mutual information, which becomes impractical for systems with high modulation orders and large antenna arrays .
Deep Learning Approaches to Linear Precoding
Data-Driven Deep Learning Models
Recent advancements propose using deep learning to address the computational challenges of linear precoding in MIMO systems. By training deep neural networks (DNNs) offline on a set of MIMO channel matrices, these models can learn the optimal precoding solutions. This approach significantly reduces the computational complexity during the online inference phase, making it feasible for real-time applications .
Model-Driven Deep Learning for Massive MU-MIMO
For massive multiuser MIMO (MU-MIMO) systems, a model-driven deep learning network has been developed. This network unfolds an iterative algorithm into a deep learning architecture, providing robustness to channel estimation errors and improving performance under Rayleigh fading channels. The proposed DL-based precoder outperforms traditional techniques in terms of complexity and robustness.
Secure Transmission in MIMOME Wiretap Channels
In secure MIMO communication scenarios, such as MIMOME wiretap channels, deep learning-based precoding can maximize the secrecy rate. DNNs are used to solve the power allocation problem efficiently, achieving optimal performance with reduced complexity compared to conventional methods .
Performance and Efficiency Gains
Numerical Results and Simulation Studies
Simulation results across various studies demonstrate that deep learning-based precoders can achieve near-optimal performance with significantly lower computational complexity. For instance, a DNN trained to approximate the input-output relationship of a nearly optimal precoder can offer similar performance to traditional methods like the interior-point method (IPM) but with much higher efficiency, especially in scenarios with higher modulation and more antennas .
Robustness and Adaptability
Deep learning-based precoders also show robustness to different MIMO scenarios and channel conditions. They can adapt to various antenna configurations and modulation schemes, making them versatile for practical communication systems .
Conclusion
Deep learning-based linear precoding for MIMO channels with finite-alphabet signaling presents a promising solution to the computational challenges of traditional methods. By leveraging the function approximation capabilities of DNNs, these approaches offer significant improvements in efficiency, performance, and robustness, making them suitable for real-time and practical applications in modern wireless communication systems.
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