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Deep-Learning-based Millimeter-Wave Massive MIMO for Hybrid Precoding
Hongji Huang, Yiwei Song, Jie Yang, Guan Gui, Fumiyuki Adachi
TL;DR
Existing mmWave massive MIMO hybrid precoding schemes are limited by high computational complexity and insufficient use of spatial information. The paper proposes a DNN-based framework that learns precoder mappings during training, and simulations report improved BER, spectrum efficiency, and convergence relative to conventional schemes.
Problem
Existing hybrid precoding schemes have high computational complexity and fail to fully exploit spatial information in mmWave massive MIMO.
Method
The paper uses a DNN-based framework that learns mapping relations for selecting hybrid analog and digital precoders during training.
Results
The DNN-based scheme outperforms conventional schemes in BER and spectrum efficiency, while the proposed and sparse schemes converge at around 11 iterations versus about 22 for analog precoding.
Takeaways & Limitations
The study concludes that DNN recognition and mapping abilities can facilitate hybrid precoding while reducing computational complexity and leveraging spatial statistics.
Abstract
from arXiv · showhide
Millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) has been regarded to be an emerging solution for the next generation of communications, in which hybrid analog and digital precoding is an important method for reducing the hardware complexity and energy consumption associated with mixed signal components. However, the fundamental limitations of the existing hybrid precoding schemes is that they have high computational complexity and fail to fully exploit the spatial information. To overcome these limitations, this paper proposes, a deep-learning-enabled mmWave massive MIMO framework for effective hybrid precoding, in which each selection of the precoders for obtaining the optimized decoder is regarded as a mapping relation in the deep neural network (DNN). Specifically, the hybrid precoder is selected through training based on the DNN for optimizing precoding process of the mmWave massive MIMO. Additionally, we present extensive simulation results to validate the excellent performance of the proposed scheme. The results exhibit that the DNN-based approach is capable ofminimizing the bit error ratio (BER) and enhancing spectrum efficiency of the mmWave massive MIMO, which achieves better performance in hybrid precoding compared with conventional schemes while substantially reducing the required computational complexity.
I. INTRODUCTION
The paper motivates deep-learning-based hybrid precoding for mmWave massive MIMO because existing approaches face high computational complexity and poor system performance. It proposes a DNN framework intended to capture structural information and improve precoding efficiency.
- mmWave massive MIMO is considered a potential technique for enhancing system throughput.
- Hybrid precoding was proposed to multiplex many data streams and achieve more accurate beamforming in mmWave massive MIMO.
- Existing hybrid precoding approaches face extraordinarily high computational complexity and poor system performance.
- Deep learning is presented as a tool for handling complicated nonlinear and high-computation problems through recognition and representation abilities.
- The paper integrates deep learning into hybrid precoding for mmWave MIMO systems.
- The proposed DNN is treated as an autoencoder and black box that learns mapping relations for hybrid precoding, with training intended to lower computational complexity.
II. SYSTEM MODEL
The system model describes a mmWave massive MIMO link with hybrid analog and digital processing. Its limited-scattering channel is modeled as low rank, motivating the use of limited RF chains and spatially informed precoding.
- The system uses one BS with a ULA of Nt antennas and a user with Nr received antennas, while the BS sends Ns independent data streams.
- The channel matrix follows the Saleh-Valenzuela model with non-line-of-sight components and steering vectors for the arrays.
- The mmWave channel has a low-rank characteristic because of limited scattering, so near-optimal throughput can be achieved with limited RF chains.
- Hybrid processing combines a high-dimensional analog precoder with a low-dimensional digital precoder.
- Analog precoders and combiners use phase shifters, with their elements constrained accordingly.
III. PROPOSED DEEP-LEARNING-BASED HYBRID PRECODING SCHEME
The proposed scheme treats hybrid precoding as a mapping problem in which deep learning extracts spatial features and maps nonlinear operations to a hybrid precoder.
- The framework treats the mmWave massive MIMO system as a black box to capture useful spatial features for hybrid precoding.
A. Proposed Deep Neural Network Architecture
The proposed DNN is a multilayer perceptron that uses hidden layers and nonlinear activation functions to learn mappings from input data to output data.
- A DNN is considered a multilayer perceptron with many hidden layers that enhance learning and mapping abilities.
- The network generates outputs from hidden-layer units using nonlinear activation functions such as ReLU and Sigmoid.
- The neural network is described using n for the number of layers and w for the network weights.
- The network architecture includes a fully connected input layer, encoding hidden layers, a noise layer, decoding hidden layers, and an output layer.
B. Learning Policy
The learning policy represents hybrid coding as a DNN mapping and trains it to obtain optimized analog and digital precoders for varying channel conditions. Training uses structural channel information, unsupervised data, and an MSE-based loss under precoder constraints.
- The GMD method decomposes the complex mmWave massive MIMO channel matrix to simplify the hybrid-coding mapping.
- The DNN is trained as a mapping operation that extracts structural statistics from mmWave channel conditions.
- The training dataset Ω contains structural model features, input data sequences, and DNN outputs, using an unsupervised learning approach.
- For each channel condition, testing produces the optimal analog precoder R_A and digital precoder R_D after training.
- The loss evaluates the mismatch between the target precoder R_1 and the hybrid product R_AR_D using mean square error.
C. Complexity Analysis
The paper identifies reduced computational complexity as a key advantage of the deep-learning-based hybrid precoding method. Algorithm 1 trains and updates the precoders iteratively until the error threshold is met, then returns the optimized precoder.
- The proposed hybrid precoding method lowers computational complexity compared with the complexity concerns motivating the study.
- Table I reports the computational complexity of several precoding schemes for mmWave massive MIMO.
- Algorithm 1 initializes the optimization, generates training sequences and channel angles, constructs the DNN, and simulates distorted or noisy wireless channels.
- Training applies stochastic gradient descent with momentum while the error remains above τ, then updates R_A and R_D.
- The procedure obtains the output-layer bias between R_1 and R_AR_D and returns the optimized precoder R_1.
IV. NUMERICAL RESULTS AND ANALYSIS
Numerical evaluations compare the proposed DNN-based hybrid precoding scheme with conventional methods across BER, spectrum efficiency, convergence, batch sizes, and learning rates. The DNN-based method reportedly improves BER and spectrum efficiency, while batch size and learning rate affect convergence and performance.
- BER performance: The DNN-based scheme outperforms the SVD- and GMD-based comparison methods in BER performance.The evaluation compares the proposed method with SVD-based hybrid, fully digital SVD-based, fully GMD-based, and new GMD-based precoding schemes.
- Training parameters: Larger batch sizes degrade BER performance by slowing convergence, while excessively small batch sizes produce unstable convergence.The paper therefore recommends selecting the batch size carefully for optimal precoding performance.
- Training parameters: Lower learning rates improve hybrid precoding performance but induce slower convergence, leaving the best learning-rate selection open.The paper attributes poorer performance at larger learning rates to higher validation error.
- Spectrum efficiency: The proposed DNN-based scheme achieves higher spectrum efficiency than spatially sparse and fully digital GMD-based precoding, with a widening gap as SNR increases.Spectrum efficiency improves with SNR for all evaluated schemes.
- Convergence and accuracy: The DNN-based and sparse precoding schemes converge at around 11 iterations, whereas the analog precoding scheme requires about 22 iterations.The proposed scheme also shows superior MSE performance relative to the compared methods.
V. CONCLUSIONS
The paper presents a deep-learning-based hybrid precoding method for mmWave massive MIMO that targets computational complexity and uses spatial statistics. Analytical results indicate that DNN recognition and mapping abilities can facilitate hybrid precoding.
- The proposed method targets lower computational complexity and improved hybrid precoding performance in mmWave massive MIMO.It is designed to leverage spatial statistics from large antenna systems.
- The method uses deep learning to perform hybrid precoding through recognition and mapping abilities.The paper presents a detailed deep-learning-based hybrid precoding method.
- Analytical results were presented to verify the performance of the DNN-based method.The results reveal that DNN can facilitate hybrid precoding.