Name
Abstract | ||
|---|---|---|
The Doppler effect is typically an impairment for wireless communications in mobile-to-mobile environments. Multipath effects leading to delay dispersion at the receiver can create a challenging doubly selective time-frequency channel response. In Vehicle-to-Vehicle (V2V) communications, small-scale effects of the V2V channel over short durations are well understood. However, observing the Doppler response over longer durations can provide observations directly related to the vehicle dynamics. Understanding the channel contributions of the Doppler response in the macro could enable context aware Doppler sensing leading to enhanced collision avoidance or location based services. To model the large-scale Doppler channel response, geometry-based stochastic channel models (GSCM) provide the best representation of how the Doppler profile is changing with the vehicle motion (or lack of). In this novel work, we build upon an existing GSCM by including the double-bounced scattering off the participating vehicles themselves and validating these contributions through real-world experimentation. The model enables the study of a potentially new "sensor" in connected vehicle networks: the large-scale Doppler response, in which mobile scatters are observable and the V2V radio can be aware of the surrounding communication environment. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/MASS.2017.66 | 2017 IEEE 14th International Conference on Mobile Ad Hoc and Sensor Systems (MASS) |
Keywords | Field | DocType |
Large-scale Doppler,Doppler shift,channel modelling,vehicle-to-vehicle | Multipath propagation,Wireless,Computer science,Location-based service,Communication channel,Real-time computing,Vehicle dynamics,Vehicle-to-vehicle,Doppler effect,Orthogonal frequency-division multiplexing,Distributed computing | Conference |
ISSN | ISBN | Citations |
2155-6806 | 978-1-5386-2325-1 | 0 |
PageRank | References | Authors |
0.34 | 11 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| billy kihei | 1 | 6 | 2.53 |
| John A. Copeland | 2 | 456 | 60.84 |
| Yusun Chang | 3 | 13 | 4.71 |