Uppsala universitet

Connected Vehicles that use Channel Prediction Will Fully Take Advantage of 5G.

Dhin Thuy Phan-Huy, Orange,
Tommy Svensson, Chalmers,
Mikael Sternad, Uppsala University,
Wolfgang Zirwas, Nokia Bell Labs,
Bernadette Villeforceix, Orange,
Fatima Karim, Orange, and
Berna Sayrac, Orange.

22nd ITS World Congress , Bordeaux, France, 5-9 October 2015.

In the future, mobile networks will support masses of connected passengers. Successive generations of mobile networks have conveyed a rising traffic by increasingly exploiting the concept of channel state information at the transmitter. This information enables advanced signal processing techniques to lower the cost of a given data rate by orders of magnitude.

Unfortunately, it is not robust to velocity. Beyond a limiting velocity, mobile networks fall back to less advanced techniques, with much higher costs. From a mobile network operator perspective, this cost gap is equivalent to a huge “wall of speed” to be climbed to connect the passengers. At high load, cost-effective connections may be prioritised.

However, we believe that connected vehicles that are co-designed by vehicular and mobile networks manufacturers and use advanced features such as “channel prediction”, will pass through the wall of speed and fully benefit from 5G.

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

Report (2016) on performance results when using the predictor antennas on a very large set of vehicular channel measurements.

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

IEEE Intelligent Transport Systems Magazine 2015 on making 5G adaptive antennas work for very fast moving vehicles.

Moving Cells Communication Magazine paper, 2013.


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