Performance Dependency of LSTM and NAR Beamformers With Respect to Sensor Array Properties in V2I Scenario

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.