Abstract
In order to realize reliable Vehicle-to-Vehicle (V2V) communication systems for autonomous driving, the recognition of radio propagation becomes an important technology. However, in the current wireless distributed network systems, it is difficult to accurately estimate the radio propagation characteristics because of the locality of the radio propagation caused by surrounding buildings and geographical features. In this paper, we propose a measurement-based radio environment database for improving the accuracy of the radio environment estimation in the V2V communication systems. The database first gathers measurement datasets of the received signal strength indicator (RSSI) related to the transmission/reception locations from V2V systems. By using the datasets, the average received power maps linked with transmitter and receiver locations are generated. We have performed measurement campaigns of V2V communications in the real environment to observe RSSI for the database construction. Our results show that the proposed method has higher accuracy of the radio propagation estimation than the conventional path loss model-based estimation.
Highlights
Along with the rapid development of wireless communication technology, the number of mobile terminals has significantly increased during the last decade
There are several methods for the map construction such as empirical path loss models and ray tracing, we focus on the measurement-based approach
After several maps are shown as examples, the accuracy of the database is evaluated by using Root Mean Squared Error (RMSE)
Summary
Along with the rapid development of wireless communication technology, the number of mobile terminals has significantly increased during the last decade. It is difficult to predict the site-specific propagation components such as shadowing in the wireless distributed network situations where both transmitter and receiver have arbitrary locations. We propose a novel architecture of the measurement-based radio environment database for the wireless distributed network situations. The datasets of different transmitters and different receivers are gathered by users obtaining from the usual communication packets Such kind of data gathering enabled by distributed users is called crowdsourcing. Note that this paper is a revised and extend version of our work presented in Reference [23] Based on this initial study, we newly discuss the proposed database-assisted power control in order to. In the discussion of the accuracy of the proposed method, a comparison of accuracy with other path loss models, the Okumura–Hata model and the two-ray path loss model is newly added
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