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

Muhammad Baqer Mollah, Honggang Wang, Hua Fang

TL;DR

The paper tackles the challenge of beam alignment overhead in 60 GHz mmWave V2V communications due to narrow beams in highly dynamic scenarios. It introduces a CNN-based model that leverages out-of-band GPS position information to predict a top-$M$ set of candidate beams, thereby reducing the beam search space while maintaining link quality. Evaluations on the real-world DeepSense6G dataset demonstrate that top-1 beams yield up to 84.58% of the ground-truth received power on average and improve accuracy over a location-fingerprint baseline by substantial margins. The approach offers a practical path to faster, more reliable V2V mmWave links by exploiting readily available position data, with potential extensions to fuse multiple sensing modalities.

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.

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

TL;DR

The paper tackles the challenge of beam alignment overhead in 60 GHz mmWave V2V communications due to narrow beams in highly dynamic scenarios. It introduces a CNN-based model that leverages out-of-band GPS position information to predict a top- set of candidate beams, thereby reducing the beam search space while maintaining link quality. Evaluations on the real-world DeepSense6G dataset demonstrate that top-1 beams yield up to 84.58% of the ground-truth received power on average and improve accuracy over a location-fingerprint baseline by substantial margins. The approach offers a practical path to faster, more reliable V2V mmWave links by exploiting readily available position data, with potential extensions to fuse multiple sensing modalities.

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.
Paper Structure (12 sections, 8 equations, 4 figures)

This paper contains 12 sections, 8 equations, 4 figures.

Table of Contents

  1. Introduction
  2. System Model and Problem Statement
  3. System Model
  4. Problem Statement
  5. Proposed Deep Learning Model
  6. Position Data Preprocessing
  7. Proposed Model Architecture
  8. Experimental Results
  9. Datasets Description
  10. Implementation Details
  11. Performance Evaluation
  12. Conclusion

Figures (4)

  • Figure 1: Illustration of considered mmWave enabled V2V communications system model.
  • Figure 2: Proposed deep learning model for mmWave beamforming.
  • Figure 3: Visual representation of receiver vehicle’s GPS location data points (400 samples) along with corresponding best beam indices out of 64 beams on Google Map satellite view.
  • Figure 4: Performance comparison of average achieved accuracies and received power ratio in percentage for all considered vehicle-to-vehicle scenarios.