A Model-Driven Deep Learning Network for MIMO Detection (1809.09336v1)

Abstract: In this paper, we propose a model-driven deep learning network for multiple-input multiple-output (MIMO) detection. The structure of the network is specially designed by unfolding the iterative algorithm. Some trainable parameters are optimized through deep learning techniques to improve the detection performance. Since the number of trainable variables of the network is equal to that of the layers, the network can be easily trained within a very short time. Furthermore, the network can handle time-varying channel with only a single training. Numerical results show that the proposed approach can improve the performance of the iterative algorithm significantly under Rayleigh and correlated MIMO channels.

• The paper introduces OAMP-Net, a method that unfolds the iterative OAMP algorithm into a trainable deep network for MIMO detection.
• It leverages learnable parameters to adapt to channel variations while maintaining low computational complexity.
• Results demonstrate significant SNR gains in both Rayleigh and correlated channels, enhancing overall detection performance.

A Model-Driven Deep Learning Network for MIMO Detection

The paper presents a sophisticated model-driven deep learning framework, termed OAMP-Net, for efficient detection in multiple-input multiple-output (MIMO) systems. Translating the iterative orthogonal AMP (OAMP) algorithm into a neural network, the paper innovatively combines deep learning with established signal processing methodologies to enhance MIMO detection performance.

Overview

MIMO technology is integral to modern wireless communication, offering notable improvements in spectral efficiency and link reliability. Optimal MIMO detection, through methods such as maximum likelihood, suffers from prohibitively high complexity. Consequently, suboptimal methods like zero-forcing and minimum mean-squared error (MMSE) are commonly employed despite their performance limitations. The paper addresses this by integrating changes into the OAMP algorithm to form a deep learning model with improved detection abilities for Rayleigh fading channels.

Methodology

OAMP-Net is designed by unfolding the iterative OAMP algorithm and applying trainable parameters to its neural network layers. Key features include:

• Iterative Unfolding: The network replicates the iterative steps of the OAMP algorithm within its architecture, allowing adjustable parameterization through learning.
• Learning Parameters: By treating some algorithmic parameters as learnable variables in the neural network, the model adapts more readily to channel variations.
• Low Complexity Training: With the number of trainable parameters being directly proportional to the number of layers (and not the system dimensions), training remains computationally feasible even for extensive MIMO systems.

Results

The empirical evaluation indicates that the proposed OAMP-Net substantially outperforms the stand-alone OAMP algorithm in both Rayleigh and correlated channel conditions. These improvements highlight the network's ability to effectively learn from data and adjust its parameters for optimal performance.

Numerical Results

• Rayleigh MIMO Channels: OAMP-Net demonstrates robust performance enhancements over OAMP. In setups with 4, 8, and 64 antennas, OAMP-Net exhibits SNR gains of 1.37 dB, 2.97 dB, and 0.82 dB, respectively, at a BER of $10^{-3}$.
• Correlated Channels: While correlated channels degrade performance compared to Rayleigh channels, OAMP-Net still provides consistent improvements over OAMP, demonstrating the model's effectiveness across varying channel states.

Implications and Future Work

The integration of deep learning techniques with model-based algorithms forms a promising arena for improved signal processing in wireless communications. This approach not only leverages the strengths of deep learning but also ensures interpretability, drawing from well-understood physical-layer processing techniques. Future developments could explore further enhancement by incorporating adaptive learning frameworks or extending to other communication frameworks beyond MIMO.

In conclusion, OAMP-Net is a significant step forward in MIMO detection, exemplifying the potential of model-driven deep learning strategies to unlock new levels of performance in complex communication environments.