Abstract—Millimeter wave wireless spectrum deployments
will allow vehicular communications to share high data rate
vehicular sensor data in real-time.The highly directional nature
of wireless links in millimeter spectral bands will require
continuous channel measurements to ensure the transmitter
(TX) and receiver (RX) beams are aligned to provide the best
channel. Using real-world vehicular mmWave measurement data
at 28GHz, we determine the optimal beam sweeping period,
i.e. the frequency of the channel measurements,to align the
RX beams to the best channel directions for maximizing the
vehicle-to-infrastructure (V2I) throughput.We show that in a
realistic vehicular traffic environment in Austin,TX, for a vehicle
traveling at an average speed of 10.5mph,a beam sweeping
period of 300 ms in future V2I communication standards would
maximize theV2I throughput,using a system of four RX phased
arrays that scanned the channel 360 degrees in the azimuth and
30 degrees above and below the boresight.We also investigate
the impact of the number of active RX chains controlling
the steerable phased arrays on V2I throughput. Reducing the
number of RX chains controlling the phased arrays helps reduce
the cost of the vehicular mmWave hardware while multiple RX
chains, although more expensive,provide more robustness to
beam direction changes at the vehicle,allowing near maximum
throughput over a wide range of beam sweep periods.We show
that the overhead of utilizing one RX chain instead of four
leads to a10% drop in mean V2I throughput over six non-line-
of-sight runs in real traffic conditions, with each run being 10
to 20 seconds long over a distance of 40 to 90 meters.
Index Terms—mmWave;beam management;channel sound-
ing; phased arrays;V2X;V2V;5G;sidelink
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Machine Learning Based Handovers for Sub-6 GHz and mmWave Integrated Vehicular Networks
The integration of sub-6 GHz and millimeter wave (mmWave) bands has a great potential to enable both reliable coverage and high data rate in future vehicular networks. Nevertheless, during mmWave vehicle-to-infrastructure (V2I) handovers, the coverage blindness of directional beams makes it a significant challenge to discover target mmWave remote radio units (mmW-RRUs) whose active beams may radiate somewhere that handover vehicles are not in. Besides, fast and soft handovers are also urgently needed in vehicular networks. Based on these observations, to solve the target discovery problem, we utilize channel state information (CSI) of sub-6 GHz bands and Kernel-based machine learning (ML) algorithms to predict vehicles’ positions and then use them to pre-activate target mmW-RRUs. Considering that the regular movement of vehicles on almost linearly paved roads with finite corner turns will generate some regularity in handovers, to accelerate handovers, we propose to use historical handover data and K-nearest neighbor (KNN) ML algorithms to predict handover decisions without involving time-consuming target selection and beam training processes. To achieve soft handovers, we propose to employ vehicle-to-vehicle (V2V) connections to forward data for V2I links. Theoretical and simulation results are provided to validate the feasibility of the proposed schemes.
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- Award ID(s):
- 1343356
- NSF-PAR ID:
- 10112937
- Date Published:
- Journal Name:
- IEEE Transactions on Wireless Communications
- ISSN:
- 1536-1276
- Page Range / eLocation ID:
- 1 to 1
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/TWC.2019.2930193
