# MmWave System for Future ITS: A MAC-layer Approach for V2X Beam Steering

## TL;DR

This paper introduces SAMBA, a MAC-layer beam alignment algorithm for mmWave V2X communications that leverages vehicle motion prediction to reduce overhead and improve data rates in intelligent transportation systems.

## Contribution

The paper proposes SAMBA, a novel MAC-layer approach that uses DSRC beacon information for overhead-free beam alignment in vehicular mmWave systems, outperforming existing strategies.

## Key findings

- SAMBA doubles data rates in sparse vehicle scenarios.
- SAMBA significantly improves throughput in dynamic vehicular environments.
- SAMBA outperforms IEEE 802.11ad beamforming strategies.

## Abstract

Millimeter Waves (mmWave) systems have the potential of enabling multi-gigabit-per-second communications in future Intelligent Transportation Systems (ITSs). Unfortunately, because of the increased vehicular mobility, they require frequent antenna beam realignments - thus significantly increasing the in-band Beamforming (BF) overhead. In this paper, we propose Smart Motion-prediction Beam Alignment (SAMBA), a MAC-layer algorithm that exploits the information broadcast via DSRC beacons by all vehicles. Based on this information, overhead-free BF is achieved by estimating the position of the vehicle and predicting its motion. Moreover, adapting the beamwidth with respect to the estimated position can further enhance the performance. Our investigation shows that SAMBA outperforms the IEEE 802.11ad BF strategy, increasing the data rate by more than twice for sparse vehicle density while enhancing the network throughput proportionally to the number of vehicles. Furthermore, SAMBA was proven to be more efficient compared to legacy BF algorithm under highly dynamic vehicular environments and hence, a viable solution for future ITS services.