TY - GEN
T1 - MmWave System for Future ITS
T2 - 86th IEEE Vehicular Technology Conference, VTC Fall 2017
AU - Mavromatis, Ioannis
AU - Tassi, Andrea
AU - Piechocki, Robert J.
AU - Nix, Andrew
PY - 2018/4
Y1 - 2018/4
N2 - 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.
AB - 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.
KW - Beamforming
KW - Connected autonomous vehicles
KW - Heterogeneity
KW - MAC layer
KW - MmWave
KW - Vehicle-to-everything communications.
UR - http://www.scopus.com/inward/record.url?scp=85045258522&partnerID=8YFLogxK
U2 - 10.1109/VTCFall.2017.8288267
DO - 10.1109/VTCFall.2017.8288267
M3 - Conference Contribution (Conference Proceeding)
SN - 9781509059362
SP - 2088
EP - 2093
BT - 2017 IEEE 86th Vehicular Technology Conference (VTC-Fall)
PB - Institute of Electrical and Electronics Engineers (IEEE)
Y2 - 24 September 2017 through 27 September 2017
ER -