Multi-Node Vehicular Wireless Channels: Measurements, Large Vehicle Modeling, and Hardware-in-the-Loop Evaluation

Abstract

Understanding multi-node vehicular wireless communication channels is crucial for future time-sensitive safety applications for human-piloted as well as partly autonomous vehicles on roads, railways and in the air. These highly dynamic wireless communication channels are characterized by rapidly changing channel statistics. In this paper we present the first fully mobile multi-node vehicular wireless channel sounding system, which is capable of simultaneously capturing multiple channel frequency responses, ensuring that measurement conditions are identical for all observed links. We use it to analyze road scenarios with multiple vehicles and a large obstructing double-decker bus. The empirical measurement data is used to parametrize a model for the large obstructing vehicle within a geometry-based stochastic channel model. We compare the time-variant channel statistics obtained from our channel model with the ones from the measurement campaign. By means of a channel emulator we obtain the packet error rates of commercial modems for the measured and the simulated wireless communication channels and compare them, in order to validate the model at the link level. We find that the path loss, the root mean square (RMS) delay spread, and the RMS Doppler spread deviate by less than 3.6 dB, 78 ns, and 52 Hz, respectively, for 80% of the total simulation duration. The PER obtained from measured data is within the maximum and minimum bounds of our model for 86% of the simulation duration.

Distributed MIMO Measurements for Integrated Communication and Sensing in an Industrial Environment

“…The size of the stationarity region is denoted with M snapshots in the time domain and N samples in the frequency domain. Following [ 19 ], we use and henceforth drop . The local scattering function is estimated as in [ 35 ]: where is the delay index, and is the Doppler index.…”

“…With a measurement setup in place, channel measurements are needed to extract the relevant parameters for realistic channel models. In [ 18 , 19 , 20 , 21 , 22 ], measurements of distributed channels were made. For the topic of joint communication and sensing, work has been done mainly in the mmWave bands in [ 23 , 24 ].…”

Machine Learning Approaches for Radio Propagation Modeling in Urban Vehicular Channels

“…Vehicular channels differ significantly from cellular communication channels due to the constantly changing surroundings in which they operate. Traditional approaches for vehicular channel modeling include measurement based empirical models [1]- [4], non-geometry based stochastic models [5]- [7], geometry based stochastic models [8]- [11], and ray-based deterministic models [12]- [14].…”