Beamforming Design for Large-Scale Antenna Arrays Using Deep Learning (1904.03657v2)

Abstract: Beamforming (BF) design for large-scale antenna arrays with limited radio frequency chains and the phase-shifter-based analog BF architecture, has been recognized as a key issue in millimeter wave communication systems. It becomes more challenging with imperfect channel state information (CSI). In this letter, we propose a deep learning based BF design approach and develop a BF neural network (BFNN) which can be trained to learn how to optimize the beamformer for maximizing the spectral efficiency with hardware limitation and imperfect CSI. Simulation results show that the proposed BFNN achieves significant performance improvement and strong robustness to imperfect CSI over the conventional BF algorithms.

• The paper proposes a Beamforming Neural Network (BFNN) designed to optimize analog beamforming for large-scale antenna arrays under challenging conditions like imperfect channel state information.
• It develops a novel neural network architecture that directly outputs optimized analog beamformers using an SE-based loss function and a two-stage design for robustness against imperfect CSI.
• Simulation results demonstrate that the BFNN significantly improves spectral efficiency compared to traditional methods, particularly in scenarios with inaccurate channel information, highlighting its practical applicability in mmWave systems.

Beamforming Design for Large-Scale Antenna Arrays Using Deep Learning

The paper titled "Beamforming Design for Large-Scale Antenna Arrays Using Deep Learning" explores the process of optimizing beamforming algorithms for large-scale millimeter-wave (mmWave) communication systems using deep learning methods. This paper is significant as it adds a novel perspective to handling the challenges associated with hardware limitations and imperfect channel state information (CSI) in real-world scenarios, such as those encountered in mmWave communications.

Background and Importance

Beamforming (BF) design has emerged as a pivotal concern in mmWave communication systems, especially when dealing with large-scale antenna arrays. The inherent challenges presented by the phase-shifter-based analog BF architecture are exacerbated when operating under conditions of imperfect CSI. Traditional hybrid analog and digital beamforming (HBF) methods have grappled with these issues, often necessitating complex serial iterations or model-based approximations, which assume perfect CSI—an ideal not typically met in practice.

The appeal of deep learning (DL) techniques lies in their potential to offer a more robust solution to the non-convex optimization problems presented by beamforming design. The capacity for DL to adapt to real-world imperfections in CSI and rapidly compute solutions through parallel processing is particularly valuable for high-speed communication systems.

Contributions

This paper presents a Beamforming Neural Network (BFNN) designed specifically to address the complexities of BF in large-scale antenna arrays with the aforementioned limitations. Key contributions of the paper include:

1. Design Approach: The paper departs from traditional full-digital design methods by proposing a neural network that directly outputs the optimized analog beamformer based on estimated CSI. This approach circumvents the need for model-based approximations by leveraging the BFNN’s ability to learn from a large number of channel estimates and corresponding spectral efficiency (SE) outcomes.
2. Loss Function: It introduces a nuanced loss function intricately connected to SE performance, diverging from conventional methods that rely on mean square error (MSE).
3. Robustness: A two-stage design method is crafted to imbue the BFNN with resistance to imperfect CSI. During offline training, the network learns to match ideal SE as closely as possible using only practical CSI estimates. This ensures that, once deployed, the BFNN remains robust amid channel uncertainties.

Simulation Results

Simulation analyses underline the BFNN’s marked improvement over traditional BF methods in terms of SE. This superiority becomes more pronounced as CSI accuracy diminishes, signaling the BFNN's strength in navigating imperfect real-world data effectively. This capability positions the BFNN as a highly applicable tool for systems dealing with sparse, complex propagation channels typical of mmWave communications.

Implications and Future Work

Practically, the application of DL in BF design can considerably enhance communication system efficiency, allowing for greater data throughput with reduced latency. Theoretically, these promising results invite further investigations into DL-enhanced communication frameworks.

Future work may explore extending the BFNN to more intricate BF scenarios, such as multi-user or multi-input multi-output (MIMO) systems. Additionally, dissecting the BFNN’s layers for deeper physical interpretations could offer fresh insights into the architecture's capabilities, unlocking potential optimizations.

In summary, this work arms researchers and system designers with innovative tools and methods that harness the power of deep learning to address critical issues in modern wireless communications infrastructure, paving the way for more resilient and effective designs.