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Abstract—Millimeter wave wireless spectrum deployments will allow vehicular communications to share high data rate vehicular sensor data in real-time.The highly directional nature of wireless links in millimeter spectral bands will require continuous channel measurements to ensure the transmitter (TX) and receiver (RX) beams are aligned to provide the best channel. Using real-world vehicular mmWave measurement data at 28GHz, we determine the optimal beam sweeping period, i.e. the frequency of the channel measurements,to align the RX beams to the best channel directions for maximizing the vehicle-to-infrastructure (V2I) throughput.We show that in a realistic vehicular traffic environment in Austin,TX, for a vehicle traveling at an average speed of 10.5mph,a beam sweeping period of 300 ms in future V2I communication standards would maximize theV2I throughput,using a system of four RX phased arrays that scanned the channel 360 degrees in the azimuth and 30 degrees above and below the boresight.We also investigate the impact of the number of active RX chains controlling the steerable phased arrays on V2I throughput. Reducing the number of RX chains controlling the phased arrays helps reduce the cost of the vehicular mmWave hardware while multiple RX chains, although more expensive,provide more robustness to beam direction changes at the vehicle,allowing near maximum throughput over a wide range of beam sweep periods.We show that the overhead of utilizing one RX chain instead of four leads to a10% drop in mean V2I throughput over six non-line- of-sight runs in real traffic conditions, with each run being 10 to 20 seconds long over a distance of 40 to 90 meters. Index Terms—mmWave;beam management;channel sound- ing; phased arrays;V2X;V2V;5G;sidelink
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This content will become publicly available on January 1, 2026
Multi-Modality Sensing in mmWave Beamforming for Connected Vehicles Using Deep Learning
Beamforming techniques are considered as essential parts to compensate severe path losses in millimeter-wave (mmWave) communications. In particular, these techniques adopt large antenna arrays and formulate narrow beams to obtain satisfactory received powers. However, performing accurate beam alignment over narrow beams for efficient link configuration by traditional standard defined beam selection approaches, which mainly rely on channel state information and beam sweeping through exhaustive searching, imposes computational and communications overheads. And, such resulting overheads limit their potential use in vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications involving highly dynamic scenarios. In comparison, utilizing out-of-band contextual information, such as sensing data obtained from sensor devices, provides a better alternative to reduce overheads. This paper presents a deep learning-based solution for utilizing the multi-modality sensing data for predicting the optimal beams having sufficient mmWave received powers so that the best V2I and V2V line-of-sight links can be ensured proactively. The proposed solution has been tested on real-world measured mmWave sensing and communication data, and the results show that it can achieve up to 98.19% accuracies while predicting top-13 beams. Correspondingly, when compared to existing been sweeping approach, the beam sweeping searching space and time overheads are greatly shortened roughly by 79.67% and 91.89%, respectively which confirm a promising solution for beamforming in mmWave enabled communications.
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- Award ID(s):
- 2010366
- PAR ID:
- 10638061
- Publisher / Repository:
- IEEE Transactions on Cognitive Communications and Networking
- Date Published:
- Journal Name:
- IEEE Transactions on Cognitive Communications and Networking
- ISSN:
- 2372-2045
- Page Range / eLocation ID:
- 1 to 1
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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