Author(s)

Wang Ruhang ¹

Affiliation(s)

¹ Hefei University of Technology, Hefei, Anhui, China

Corresponding Author

Wang Ruhang

ABSTRACT

This paper aims to study various technologies based on LIDAR, including distributed architecture, vision assisted wireless communication framework, multimodal machine learning framework, and self-calibrating illumination learning framework. The optimal algorithm will be selected and improved according to the specific situation of the actual dataset. The general plan is to first enhance low brightness images affected by weather and other environmental factors, and then use deep learning models to find the mapping relationship between visual image information and optimal codewords. In formal beam training, the accuracy of codeword prediction is improved to maximize the received power and improve the performance of vehicle networking communication.

KEYWORDS

Artificial intelligence; Beam training; Vehicle communication system; Image enhancement

CITE THIS PAPER

Wang Ruhang, An Improved Design of Beam Training Scheme in  Vehicle Communication System. Journal of Artificial Intelligence Practice (2025) Vol. 8: 132-139. DOI: http://dx.doi.org/10.23977/jaip.2025.080117.

REFERENCES

[1] U. Demirhan and A. Alkhateeb, "Radar Aided 6G Beam Prediction: Deep Learning Algorithms and Real-World Demonstration," 2022 IEEE Wireless Communications and Networking Conference (WCNC), Austin, TX, USA, 2022, pp. 2655-2660. 
[2] M. Dias, A. Klautau, N. Gonzalez-Prelcic, and R. W. Heath, "Position and LIDAR-aided mmWave beam selection using deep learning," in Proc. IEEE Int. Workshop on Signal Processing Adv. in Wireless Commun. (SPAWC), Cannes, France, 2019, pp. 1-5.
[3] A. Klautau, N. Gonzalez-Prelcic, and R. W. Heath, "LIDAR data for deep learning-based mmWave beam-selection," IEEE Wireless Commun. Lett, vol. 8, no. 3, pp. 909-912, 2019. 
[4] M. Alrabeiah, A. Hredzak and A. Alkhateeb, "Millimeter Wave Base Stations with Cameras: Vision-Aided Beam and Blockage Prediction," 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring), Antwerp, Belgium, 2020, pp. 1-5.  
[5] G. Charan, T. Osman, A. Hredzak, N. Thawdar and A. Alkhateeb, "Vision-Position Multi-Modal Beam Prediction Using Real Millimeter Wave Datasets," 2022 IEEE Wireless Communications and Networking Conference (WCNC), Austin, TX, USA, 2022, pp. 2727-2731. 

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