Deep Learning and Image Super-Resolution-Guided Beam and Power Allocation for mmWave Networks

• URL: http://arxiv.org/abs/2305.13929v1
• Date: Mon, 8 May 2023 05:40:54 GMT
• Title: Deep Learning and Image Super-Resolution-Guided Beam and Power Allocation for mmWave Networks
• Authors: Yuwen Cao, Tomoaki Ohtsuki, Setareh Maghsudi, and Tony Q. S. Quek
• Abstract summary: 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.
• Score: 80.37827344656048
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we develop a deep learning (DL)-guided hybrid beam and power allocation approach for multiuser millimeter-wave (mmWave) networks, which facilitates swift beamforming at the base station (BS). The following persisting challenges motivated our research: (i) User and vehicular mobility, as well as redundant beam-reselections in mmWave networks, degrade the efficiency; (ii) Due to the large beamforming dimension at the BS, the beamforming weights predicted by the cutting-edge DL-based methods often do not suit the channel distributions; (iii) Co-located user devices may cause a severe beam conflict, thus deteriorating system performance. To address the aforementioned challenges, we exploit the synergy of supervised learning and super-resolution technology to enable low-overhead beam- and power allocation. In the first step, we propose a method for beam-quality prediction. It is based on deep learning and explores the relationship between high- and low-resolution beam images (energy). Afterward, we develop a DL-based allocation approach, which enables high-accuracy beam and power allocation with only a portion of the available time-sequential low-resolution images. Theoretical and numerical results verify the effectiveness of our proposed

Related papers

• Near-field Beamforming for Extremely Large-scale MIMO Based on Unsupervised Deep Learning [20.67122533341949]
  We propose a near-field beamforming method based on unsupervised deep learning. Our proposed scheme can obtain stable beamforming gain compared with the baseline scheme.
  arXiv  Detail & Related papers  (2024-06-05T13:26:25Z)
• Reliable Beamforming at Terahertz Bands: Are Causal Representations the Way Forward? [85.06664206117088]
  Multi-user wireless systems can meet metaverse requirements by utilizing terahertz bandwidth with massive number of antennas. Existing solutions lack proper modeling of channel dynamics, resulting in inaccurate beamforming solutions in high-mobility scenarios. Herein, a dynamic, semantically aware beamforming solution is proposed for the first time, utilizing novel artificial intelligence algorithms in variational causal inference.
  arXiv  Detail & Related papers  (2023-03-14T16:02:46Z)
• Beam Management in Ultra-dense mmWave Network via Federated Reinforcement Learning: An Intelligent and Secure Approach [19.01563068819449]
  Key challenge of ultra-dense mmWave network (UDmmWave) is beam management due to high propagation delay limited beam coverage. In this paper, a novel beam management scheme is presented which can theoretically protect user privacy while reducing handoff cost.
  arXiv  Detail & Related papers  (2022-10-04T01:47:33Z)
• Predictive GAN-powered Multi-Objective Optimization for Hybrid Federated Split Learning [56.125720497163684]
  We propose a hybrid federated split learning framework in wireless networks. We design a parallel computing scheme for model splitting without label sharing, and theoretically analyze the influence of the delayed gradient caused by the scheme on the convergence speed.
  arXiv  Detail & Related papers  (2022-09-02T10:29:56Z)
• Federated Learning over Wireless IoT Networks with Optimized Communication and Resources [98.18365881575805]
  Federated learning (FL) as a paradigm of collaborative learning techniques has obtained increasing research attention. It is of interest to investigate fast responding and accurate FL schemes over wireless systems. We show that the proposed communication-efficient federated learning framework converges at a strong linear rate.
  arXiv  Detail & Related papers  (2021-10-22T13:25:57Z)
• A Q-Learning-based Approach for Distributed Beam Scheduling in mmWave Networks [18.22250038264899]
  We consider the problem of distributed downlink beam scheduling and power allocation for millimeter-Wave (mmWave) cellular networks. Multiple base stations belonging to different service operators share the same unlicensed spectrum with no central coordination or cooperation among them. We propose a distributed scheduling approach to power allocation and adaptation for efficient interference management over the shared spectrum by modeling each BS as an independent Q-learning agent.
  arXiv  Detail & Related papers  (2021-10-17T02:58:13Z)
• Deep Reinforcement Learning Based Multidimensional Resource Management for Energy Harvesting Cognitive NOMA Communications [64.1076645382049]
  Combination of energy harvesting (EH), cognitive radio (CR), and non-orthogonal multiple access (NOMA) is a promising solution to improve energy efficiency. In this paper, we study the spectrum, energy, and time resource management for deterministic-CR-NOMA IoT systems.
  arXiv  Detail & Related papers  (2021-09-17T08:55:48Z)
• Deep Learning Assisted Calibrated Beam Training for Millimeter-Wave Communication Systems [15.297530726877786]
  Huge overhead of beam training imposes a significant challenge in millimeter-wave (mmWave) wireless communications. We propose a wide beam based training approach to calibrate the narrow beam direction according to the channel power leakage. To handle the complex nonlinear properties of the channel power leakage, deep learning is utilized to predict the optimal narrow beam directly.
  arXiv  Detail & Related papers  (2021-01-08T04:02:34Z)
• Deep Learning Methods for Universal MISO Beamforming [19.747638780327257]
  This letter studies deep learning approaches to optimize beamforming vectors in downlink multi-user multi-antenna systems. We exploit the sum power budget as side information so that deep neural networks (DNNs) can effectively learn the impact of the power constraint in the beamforming optimization.
  arXiv  Detail & Related papers  (2020-07-02T02:29:33Z)
• Optimization-driven Deep Reinforcement Learning for Robust Beamforming in IRS-assisted Wireless Communications [54.610318402371185]
  Intelligent reflecting surface (IRS) is a promising technology to assist downlink information transmissions from a multi-antenna access point (AP) to a receiver. We minimize the AP's transmit power by a joint optimization of the AP's active beamforming and the IRS's passive beamforming. We propose a deep reinforcement learning (DRL) approach that can adapt the beamforming strategies from past experiences.
  arXiv  Detail & Related papers  (2020-05-25T01:42:55Z)

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.