Learning Based Hybrid Beamforming for Millimeter Wave Multi-User MIMO Systems

• URL: http://arxiv.org/abs/2004.12917v1
• Date: Mon, 27 Apr 2020 16:31:08 GMT
• Title: Learning Based Hybrid Beamforming for Millimeter Wave Multi-User MIMO Systems
• Authors: Shaocheng Huang, Yu Ye, Ming Xiao
• Abstract summary: We propose an extreme learning machine (ELM) framework to jointly optimize transmitting and receiving beamformers. Both FP-MM-HBF and ELM-HBF can provide higher system sum-rate compared with existing methods.
• Score: 22.478350298755892
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hybrid beamforming (HBF) design is a crucial stage in millimeter wave (mmWave) multi-user multi-input multi-output (MU-MIMO) systems. However, conventional HBF methods are still with high complexity and strongly rely on the quality of channel state information. We propose an extreme learning machine (ELM) framework to jointly optimize transmitting and receiving beamformers. Specifically, to provide accurate labels for training, we first propose an factional-programming and majorization-minimization based HBF method (FP-MM-HBF). Then, an ELM based HBF (ELM-HBF) framework is proposed to increase the robustness of beamformers. Both FP-MM-HBF and ELM-HBF can provide higher system sum-rate compared with existing methods. Moreover, ELM-HBF cannot only provide robust HBF performance, but also consume very short computation time.

Related papers

• Deep Learning Based Joint Multi-User MISO Power Allocation and Beamforming Design [29.295165146832097]
  We propose a novel unsupervised deep learning based joint power allocation and beamforming design for multi-user multiple-input single-output (MU-MISO) system. We conduct experiments for diverse settings to compare the performance of NNBF-P with zero-forcing beamforming (ZFBF), minimum mean square error (MMSE) beamforming, and NNBF, which is also our deep learning based beamforming design without joint power allocation scheme.
  arXiv  Detail & Related papers  (2024-06-12T16:21:11Z)
• Deep Learning Assisted Multiuser MIMO Load Modulated Systems for Enhanced Downlink mmWave Communications [68.96633803796003]
  This paper is focused on multiuser load modulation arrays (MU-LMAs) which are attractive due to their low system complexity and reduced cost for millimeter wave (mmWave) multi-input multi-output (MIMO) systems. The existing precoding algorithm for downlink MU-LMA relies on a sub-array structured (SAS) transmitter which may suffer from decreased degrees of freedom and complex system configuration. In this paper, we conceive an MU-LMA system employing a full-array structured (FAS) transmitter and propose two algorithms accordingly.
  arXiv  Detail & Related papers  (2023-11-08T08:54:56Z)
• Deep Learning and Image Super-Resolution-Guided Beam and Power Allocation for mmWave Networks [80.37827344656048]
  We develop a deep learning (DL)-guided hybrid beam and power allocation approach for millimeter-wave (mmWave) networks. We exploit the synergy of supervised learning and super-resolution technology to enable low-overhead beam- and power allocation.
  arXiv  Detail & Related papers  (2023-05-08T05:40:54Z)
• AI-Empowered Hybrid MIMO Beamforming [85.48860461696417]
  Hybrid multiple-input multiple-output (MIMO) systems implement part of their beamforming in analog and part in digital. Recent years have witnessed a growing interest in using data-aided artificial intelligence (AI) tools for hybrid beamforming design. This article reviews candidate strategies to leverage data to improve real-time hybrid beamforming design.
  arXiv  Detail & Related papers  (2023-03-03T06:04:20Z)
• DeMFI: Deep Joint Deblurring and Multi-Frame Interpolation with Flow-Guided Attentive Correlation and Recursive Boosting [50.17500790309477]
  DeMFI-Net is a joint deblurring and multi-frame framework. It converts blurry videos of lower-frame-rate to sharp videos at higher-frame-rate. It achieves state-of-the-art (SOTA) performances for diverse datasets.
  arXiv  Detail & Related papers  (2021-11-19T00:00:15Z)
• GAN-MDF: A Method for Multi-fidelity Data Fusion in Digital Twins [82.71367011801242]
  Internet of Things (IoT) collects real-time data of physical systems, such as smart factory, intelligent robot and healtcare system. High-fidelity (HF) responses describe the system of interest accurately but are computed costly. Low-fidelity (LF) responses have a low computational cost but could not meet the required accuracy. We propose a novel generative adversarial network for MDF in digital twins (GAN-MDF)
  arXiv  Detail & Related papers  (2021-06-24T06:40:35Z)
• Federated Dropout Learning for Hybrid Beamforming With Spatial Path Index Modulation In Multi-User mmWave-MIMO Systems [19.10321102094638]
  We introduce model-based and model-free frameworks for beamformer design in SPIM-MIMO systems. The proposed framework exhibits higher spectral efficiency than the state-of-the-art SPIM-MIMO methods and mmWave-MIMO.
  arXiv  Detail & Related papers  (2021-02-15T10:49:26Z)
• Hybrid Beamforming for mmWave MU-MISO Systems Exploiting Multi-agent Deep Reinforcement Learning [27.29682248242345]
  A multi-agent DRL method is proposed to solve the exploration efficiency problem in DRL. In the proposed method, prioritized replay buffer and more informative reward are applied to accelerate the convergence.
  arXiv  Detail & Related papers  (2021-02-01T10:04:20Z)
• Joint Deep Reinforcement Learning and Unfolding: Beam Selection and Precoding for mmWave Multiuser MIMO with Lens Arrays [54.43962058166702]
  millimeter wave (mmWave) multiuser multiple-input multiple-output (MU-MIMO) systems with discrete lens arrays have received great attention. In this work, we investigate the joint design of a beam precoding matrix for mmWave MU-MIMO systems with DLA.
  arXiv  Detail & Related papers  (2021-01-05T03:55:04Z)
• MFES-HB: Efficient Hyperband with Multi-Fidelity Quality Measurements [34.75195640330286]
  We present MFES-HB, an efficient Hyperband method that is capable of utilizing both the high-fidelity and low-fidelity measurements. We show that MFES-HB can achieve 3.3-8.9x speedups over the state-of-the-art approach - BOHB.
  arXiv  Detail & Related papers  (2020-12-05T11:51:15Z)
• Learning Based Hybrid Beamforming Design for Full-Duplex Millimeter Wave Systems [22.478350298755892]
  We propose two learning schemes to design HBF for FD mmWave systems, i.e., extreme learning machine based HBF and convolutional neural networks based HBF. Results show that both learning based schemes can provide more robust HBF performance and achieve at least 22.1% higher spectral efficiency.
  arXiv  Detail & Related papers  (2020-04-16T15:48:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.