  Prediction and nullifying the interference is a challenging problem in
vehicle to infrastructure scenarios . The implementation of practical V2I
network is limited because of inevitability of interference due to random
nature of the wireless channel. The interference introduces angle ambiguity
between the road side units mounted base station and user equipment. This paper
proposes an adaptive beamforming technique for mitigation of interference in
V2I networks, especially in multiuser environment. In this work , Long short
term based (LSTM) based deep learning and non linear auto regresive technique
based regressor have been employed to predict the angles between the road side
units and user equipment .Advance prediction of transmit and receive signals
enables reliable vehicle to infrastructure communication. Instead of predicting
the beamforming matrix directly, we predict the main features using LSTM for
learning dependencies in the input time series ,where complex variables were
taken as input states and final beamformed signal was the output. simulation
results have confirmed that the proposed LSTM model achieves comparable
performance in terms of system throughput when compared with the non linear
auto regressive method implemented as an artificial neural network.
