# Experimental Demonstration of MmWave Vehicle-to-Vehicle Communications Using IEEE 802.11 ad
## 2 Related Works
provided research results on propagation characteristics of V2V channels, particularly for shadowing effects induced by obstructing vehicles between a transmitter and receiver [14]. In [15; 33], a geometric multiple-input multiple-output (MIMO) channel model for mmWave mobile-to-mobile (M2M) applications based on the two-ring reference model is proposed. Several studies on the V2X network based on a stochastic geometry model are reported that investigate and optimize the connectivity and throughput with varying blockage, beam direction and vehicle density. [PERSON] et al. proposed a stochastic model of mmWave-based RSUs infra-structure to vehicle communications [34], where they investigated the blockage probability and throughput with varying vehicle densities and speeds at a multi-lane highway. [PERSON] et al. presented a theoretical analysis of the Doppler power spectrum in the presence of beamforming at the transmitter and/or the receiver in V2I systems [35]. [PERSON] et al. analyzed the coverage of urban mmWave micro-cellular networks based on stochastic geometry with a LOS probability function of randomly oriented buildings for a V2I scenario [36]. [PERSON] et al. analyzed the interplay between the beamwidth assignment and the scheduling period in V2V communications [20] and proposed an optimization algorithm to establish a V2V link having optimal beam width, using swarm intelligence based on the channel and queue state information [21]. [PERSON] et al. proposed a swarm intelligence to efficiently pair vehicles of V2V links and optimize beam widths considering the channel state information and queue state information [22]. Some studies propose approaches to utilize sensors (such as radars, GPS, camera, etc.) to reduce the beam alignment overhead among vehicles based on vehicle position, posture, or other sensed information. [PERSON] et al. [19] and [PERSON] et al. [18] proposed a set of algorithms to perform the beam alignment in a V2I scenario, by extracting information from the IEEE 802.11 ad module or the LRR radar signal to configure the beams or create a joint waveform for automotive radars and mmWave V2V communications using the same hardware. In [16], [PERSON] et al. proposed a high-level solution for a key challenge of the mmWave beam training overhead where the information derived from the sensors or DSRC are leveraged as lateral information for the mmWave communication link configuration. [PERSON] et al. leveraged vehicle sensory data of position and the motion for beam forming by DSRC beacons [17]. Similarly, location-based beam alignment and training are considered in the following studies. [PERSON] et al. proposed a mmWave-beam switching approach based on the position information (for example, the information available via GPS) from the train control system for efficient beam alignment [37] and presented an optimization of beam design to maximize the data rate for non-overlap beams in the LOS to the RSU [38]. [PERSON] et al. also proposed a location-aided beam forming strategy and analyzed the resulting performance considering the antenna gain and latency [39]. [PERSON] et al. formulated the optimum beam alignment solution of a Bayesian team decision problem with novel and less complicated algorithms for optimality [40]. [PERSON] et al. leveraged the position of a vehicle along the past beam measurements to rank desirable pointing directions that can reduce the required beam training based on a popular machine learning method used in recommender systems [41]; moreover, they proposed the utilization of the position of the vehicle to query a multipath fingerprint database that provides prior knowledge of potential pointing directions for reliable beam alignment [42]. In [43], [PERSON] et al. also introduced machine learning with the past beam training records for optimal beam pairing by exploiting the locations and sizes of the receiver and its neighboring vehicles. [PERSON] et al.