# Experimental Demonstration of MmWave Vehicle-to-Vehicle Communications Using IEEE 802.11 ad
## 3 GiV2V Communication Architecture  
In this section, we introduce the GiV2V architecture and beam forming process according to the GiV2V topologies. Further, a well-known pathloss model of the IEEE 802.11 ad standard is introduced to verify the applicability of this pathloss model to our measurement results in Section 4. The GiV2V network topology is shown in Figure 1. The V2V communications for autonomous driving occur typically in a vehicular platoon, as illustrated in Figure 1a, in which a single driver or the front car leads multiple trailing vehicles similar to a train. In this convoy model, the GiV2V can form a front or rear beam to obtain directivity gain in mmWave communications. Thus, the connectivity between vehicles can be more stable when compared to other traffic formations as the beam direction is unchanged and inter-vehicle distance can be maintained consistently by controlling vehicle speed. In other vehicle topologies, vehicles require diagonal (i.e., front-to-side or rear-to-side) beams to communicate with vehicles in other neighboring lanes, as shown in Figure 1b, rather than just side beams to reduce the collision risk between lanes. These diagonal transmissions are required while changing lanes in the convoy model or while communicating with other convoy vehicles.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{284.5 pt}} \hline \hline
**References** & **Contents of the Studies on mmWave V2X Communications** \\ \hline
[12; 13; 14; 15; 31; 32; 33] & Vehicular channel models with link blockage, scattering, shadowing and multipath fading are established. Furthermore, reflection and diffraction of mmWave at realistic road surfaces and geometries are explored for the model. \\ \hline
[34; 35; 36; 53] & A stochastic model for V2X communications with blockage probability from RSU and throughput is presented. \\ \hline
[44; 45; 46] & Blockage detection and relay/multi-hop routing in V2X communications to avoid obstacles or extend network coverage is demonstrated. \\ \hline
[37; 38; 39; 40; 41; 42; 43] & Location or situation-based channel estimation, beam direction steering and training are achieved when prior channel information or past measurement is given for each location or situation. Machine learning techniques can be applied. \\ \hline
[18; 19] & A mmWave link is configured using Long-Range Radar (LRR) mounted on the road infrastructures and on the vehicles for V2I and V2V communications. \\ \hline
[16; 17] & The mmWave link configuration is assisted by motion and posture information of vehicles estimated from vehicular sensors. DSRC beacons carry the sensor information periodically. \\ \hline
[8; 47] & Effect of inter-beam interference in V2V networks is analyzed and beam alignment and multi-channel assignment are considered. \\ \hline
[20; 21; 22] & Distributed beam alignment and width decision are achieved by channel and queue state information. V2V association and scheduling problem are solved in a decentralized manner. \\ \hline
[48; 49] & Multi-connectivity with use of microwave frequencies (e.g., DSRC, LTE) is considered to increase the robustness connectivity (e.g., handover or relay) and reduce the beam tracking overhead. \\ \hline
[10; 11; 30] & Testbeds for mmWave V2I or V2V communications and experiment results are introduced. \\ \hline
[52] & The IEEE 802.11 ad standard (WiGig) is investigated for mmWave V2X communications using simulation. \\ \hline
[50] & Security techniques of physical layer in mmWave and MIMO systems are proposed. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Survey on mmWave-based V2X communications.