Uppsala universitet

Making 5G Adaptive Antennas Work for Very Fast Moving Vehicles.

Dhin Thuy Phan-Huy, Orange Labs, Mikael Sternad , Uppsala University and Tommy Svensson , Chalmers.

IEEE Intelligent Transportation Systems Magazine , Summer, 2015, pp. 71-84.
© 2015 IEEE

Wireless systems increasingly rely on the accurate knowledge at the transmitter side of the transmitter-to-receiver propagation channel, to optimize the transmission adaptively. Some candidate techniques for 5th generation networks need the channel knowledge for tens of antennas to perform adaptive beamforming from the base station towards the mobile terminal. These techniques reduce the radiated power and the energy consumption of the base station. Unfortunately, they fail to deliver the targeted quality of service to fast moving terminals such as connected vehicles. Indeed, due to the movement of the vehicle during the delay between channel estimation and data transmission, the channel estimate is outdated.

In this paper, we propose three new schemes that exploit the “Predictor Antenna” concept. This recent concept is based on the observation that the position occupied by one antenna at the front of the vehicle, will later on be occupied by another antenna at the back. Estimating the channel of the “front” antenna can therefore later help beamforming towards the “back” antenna.

Simulations show that our proposed schemes make adaptive beamforming work for vehicles moving at speeds up to 300 km/h.

Related publications:
IEEE WCNC 2012 on using "predictor antennas" for long-range prediction of fast fading for moving relays.

Paper in IEEE ICC 2017 on statistical performance results when using the predictor antennas on a very large set of vehicular channel measurements.

Compainon Paper in IEEE PIMRC 2017 on actual prediction results on a very large set of vehicular channel measurements.

WSA 2018 paper verifying with measurements that predictor antennas enable precise precoding for massive MIMO antennas in non-line-of sigth.

Channel Estimation and Prediction for MIMO OFDM Systems. Key design and performance aspects of Kalman-based algorithms. PhD Thesis by Daniel Aronsson, 2012.

Moving Cells Communication Magazine paper, 2013.

On the "wall of speed" for vehicular users, ITS World Congress, 2015.


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