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Attractor selection based limited feedback hybrid precoding for uplink V2I communications



Hamid Gharavi


As an essential part of vehicle networks, the Vehicle to Infrastructure (V2I) needs the support of millimeter wave and massive MIMO technologies to enable high data rate applications, such as automated driving, real-time high-quality multimedia services and so on. As the scale of the antenna array increases, the complexity of the beamforming and channel estimation algorithms under high mobility conditions also increases significantly. In particular, highly robust beamforming methods need to cope with fast changing transmission environments. In this paper, we adopt a biological inspired self-adaptive selection algorithm called attractor selection algorithm (ASA) to support uplink beamforming. The ASA requires only a little feedback information from the Road Side Infrastructure (RSI) to perform fast beam training, hence making the transmission link more stable. The simulation results indicate that the proposed ASA-assisted algorithm can significantly reduce the time required to achieve a timely beam training, which would be essential for V2I high communications under high mobility conditions.
IEEE Transactions on Vehicular Technology


5G, vehicular networks, millimeter-wave, AI, MIMO, Attractor Selection Algorithm (ASA).


Gharavi, H. (2020), Attractor selection based limited feedback hybrid precoding for uplink V2I communications, IEEE Transactions on Vehicular Technology, [online], (Accessed May 24, 2024)


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Created March 31, 2020, Updated July 30, 2020