Position Aware 60 GHz mmWave Beamforming for V2V Communications Utilizing Deep Learning

• URL: http://arxiv.org/abs/2402.01259v1
• Date: Fri, 2 Feb 2024 09:30:27 GMT
• Title: Position Aware 60 GHz mmWave Beamforming for V2V Communications Utilizing Deep Learning
• Authors: Muhammad Baqer Mollah, Honggang Wang, and Hua Fang
• Abstract summary: This paper presents a deep learning-based solution on utilizing the vehicular position information for predicting the optimal beams having sufficient mmWave received powers. The results show that the solution can achieve up to 84.58% of received power of link status on average.
• Score: 2.4993733210446893
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Beamforming techniques are considered as essential parts to compensate the severe path loss in millimeter-wave (mmWave) communications by adopting large antenna arrays and formulating narrow beams to obtain satisfactory received powers. However, performing accurate beam alignment over such narrow beams for efficient link configuration by traditional beam selection approaches, mainly relied on channel state information, typically impose significant latency and computing overheads, which is often infeasible in vehicle-to-vehicle (V2V) communications like highly dynamic scenarios. In contrast, utilizing out-of-band contextual information, such as vehicular position information, is a potential alternative to reduce such overheads. In this context, this paper presents a deep learning-based solution on utilizing the vehicular position information for predicting the optimal beams having sufficient mmWave received powers so that the best V2V line-of-sight links can be ensured proactively. After experimental evaluation of the proposed solution on real-world measured mmWave sensing and communications datasets, the results show that the solution can achieve up to 84.58% of received power of link status on average, which confirm a promising solution for beamforming in mmWave at 60 GHz enabled V2V communications.

Related papers

• Vehicle Cameras Guide mmWave Beams: Approach and Real-World V2V Demonstration [13.117333069558812]
  Accurately aligning millimeter-wave (mmWave) and terahertz (THz) narrow beams is essential to satisfy reliability and high data rates of 5G and beyond wireless communication systems. We develop a deep learning solution for V2V scenarios to predict future beams using images from a 360 camera attached to the vehicle.
  arXiv  Detail & Related papers  (2023-08-20T20:43:11Z)
• 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)
• 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)
• Fast Beam Alignment via Pure Exploration in Multi-armed Bandits [91.11360914335384]
  We develop a bandit-based fast BA algorithm to reduce BA latency for millimeter-wave (mmWave) communications. Our algorithm is named Two-Phase Heteroscedastic Track-and-Stop (2PHT&S)
  arXiv  Detail & Related papers  (2022-10-23T05:57:39Z)
• A Novel Look at LIDAR-aided Data-driven mmWave Beam Selection [24.711393214172148]
  We propose a lightweight neural network (NN) architecture along with the corresponding LIDAR preprocessing. Our NN-based beam selection scheme can achieve 79.9% throughput without any beam search overhead and 95% by searching among as few as 6 beams.
  arXiv  Detail & Related papers  (2021-04-29T18:07:31Z)
• Deep Learning-based Compressive Beam Alignment in mmWave Vehicular Systems [75.77033270838926]
  vehicular channels exhibit structure that can be exploited for beam alignment with fewer channel measurements. We propose a deep learning-based technique to design a structured compressed sensing (CS) matrix.
  arXiv  Detail & Related papers  (2021-02-27T04:38:12Z)
• Federated mmWave Beam Selection Utilizing LIDAR Data [3.7352534957395522]
  We propose distributed LIDAR aided beam selection for V2I mmWave communication systems utilizing federated training. In the proposed scheme, connected vehicles collaborate to train a shared neural network (NN) on their locally available LIDAR data during normal operation of the system. We also propose an alternative reduced-complexity convolutional NN (CNN) architecture and LIDAR preprocessing, which significantly outperforms previous works in terms of both the performance and the complexity.
  arXiv  Detail & Related papers  (2021-02-04T18:49:20Z)
• Distributional Reinforcement Learning for mmWave Communications with Intelligent Reflectors on a UAV [119.97450366894718]
  A novel communication framework that uses an unmanned aerial vehicle (UAV)-carried intelligent reflector (IR) is proposed. In order to maximize the downlink sum-rate, the optimal precoding matrix (at the base station) and reflection coefficient (at the IR) are jointly derived.
  arXiv  Detail & Related papers  (2020-11-03T16:50:37Z)
• Applying Deep-Learning-Based Computer Vision to Wireless Communications: Methodologies, Opportunities, and Challenges [100.45137961106069]
  Deep learning (DL) has seen great success in the computer vision (CV) field. This article introduces ideas about applying DL-based CV in wireless communications.
  arXiv  Detail & Related papers  (2020-06-10T11:37:49Z)
• Beamforming Learning for mmWave Communication: Theory and Experimental Validation [23.17604790640996]
  We propose a beam design technique that reduces the search time and does not require CSI while guaranteeing a minimum beamforming gain. We evaluate the efficacy of the proposed scheme in terms of building the codebook and assessing its performance through real-life measurements.
  arXiv  Detail & Related papers  (2019-12-28T05:46:39Z)

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.