A simple ANN-MLP model for estimating 60-GHz PDP inside public and private vehicles

EURASIP Journal on Wireless Communications and Networking

Research

Ales Prokes¹  Tomas Mikulasek¹  Abhishek Narayan Sarkar²  Rajeev Shukla²  Aniruddha Chandra²  Cezary Ziolkowski³  Jan M. Kelner³ 

[1] Department of Radio Electronics, Brno University of Technology, 61600, Brno, Czech Republic;ECE Department, National Institute of Technology, M. G. Avenue, 713209, Durgapur, India;Institute of Communications Systems, Faculty of Electronics, Military University of Technology, 00908, Warsaw, Poland;

关键词: Channel sounding;    Intra-vehicular communication;    Millimetre wave;    Multilayer perceptron;    Power delay profile;   

DOI  :  10.1186/s13638-023-02257-0

 received in 2022-07-22, accepted in 2023-06-01,  发布年份 2023

来源: Springer

【 摘 要 】

Radio wave propagation in an intra-vehicular (IV) environment is markedly different from other well-studied indoor scenarios, such as an office or a factory floor. While millimetre wave (mmWave)-based intra-vehicular communications promise large bandwidth and can achieve ultra-high data rates with lower latency, exploiting the advantages of mmWave communications largely relies on adequately characterising the propagation channel. Channel characterisation is most accurately done through an extensive channel sounding, but due to hardware and environmental constraints, it is impractical to test channel conditions for all possible transmitter and receiver locations. Artificial neural network (ANN)-based channel sounding can overcome this impediment by learning and estimating the channel parameters from the channel environment. We estimate the power delay profile in intra-vehicular public and private vehicle scenarios with a high accuracy using a simple feedforward multi-layer perception-based ANN model. Such artificially generated models can help extrapolate other relevant scenarios for which measurement data are unavailable. The proposed model efficiently matches the taped delay line samples obtained from real-world data, as shown by goodness-of-fit parameters and confusion matrices.

【 授权许可】

CC BY   
© The Author(s) 2023

【 图 表 】

Fig. 3

Fig. 4

Fig. 19

Fig. 18

Fig. 12

Fig. 2

Fig. 4

Fig. 2

Fig. 16

Fig. 1

Fig. 3

Fig. 14

Fig. 3

Fig. 13

Fig. 12

Fig. 1

Fig. 10

Fig. 7

Fig. 5

Fig. 1

Fig. 3

Fig. 1

Fig. 6