Deep Learning Approach for Precise Positioning of Millimeter Wave in 5G for V2V Links
- DOI
- 10.2991/978-94-6463-082-4_11How to use a DOI?
- Keywords
- V2V Technology; V2X; 5G; mmWave of 5G; Deep learning; AIoT
- Abstract
The millimeter-wave of 5G will usher in a new era in Vehicle-to-Vehicle (V2V) communication. The ensuing radiation from a millimeter-wave of 5G bounces off most visible things, creating enriched multi-directional environments, particularly in sub-urban scenarios. Physical impediments were once primarily connected with signal attenuation; nevertheless, their existence now introduces complicated, non-linear phenomena such as reflections and scattering. As a result of the impediments faced, a multipath propagation environment emerges, suggesting the presence of concealed spatial information within the received signal for a dense vehicular environment. The key contributions of this research are to discuss and evaluate a self-proposed deep neural network for the beamformed fingerprint location problem in connected cars. Training of deep learning model and simulation environment has been performed using AMD Ryzen 7 GPU environment. Results show that in a realistic outdoor sub-urban scenario with predominantly non-line-of-sight (NLoS) positions, average estimation errors of less than 1.69 m can be achieved, paving the way for novel positioning systems beneficial for V2V links with low computational power. Furthermore, the self-proposed deep learning model is compared with the long-short-term memory (LSTM) in terms of computational complexity. Self-proposed DNN outperform LSTM in terms of training time by 50 min.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - H. T. Ahmed AU - Jun Jiat Tiang AU - Azwan Bin Mahmud AU - Gwo Chin Chung PY - 2022 DA - 2022/12/23 TI - Deep Learning Approach for Precise Positioning of Millimeter Wave in 5G for V2V Links BT - Proceedings of the Multimedia University Engineering Conference (MECON 2022) PB - Atlantis Press SP - 97 EP - 107 SN - 2352-5401 UR - https://doi.org/10.2991/978-94-6463-082-4_11 DO - 10.2991/978-94-6463-082-4_11 ID - Ahmed2022 ER -