Predicting Beamforming Vectors Using LSTM Networks

This code trains an LSTM network on synthetic data to predict beamforming vectors, evaluating its performance based on simplified factors.

This code utilizes an LSTM model to predict optimal beamforming vectors for a new user in a wireless network. The model is trained on synthetic data that includes user location, signal quality, channel state information (CSI), interference levels, and corresponding beamforming vectors for each antenna. Following training, the model generates predictions of beamforming vectors for a new user. These predicted vectors are then compared with actual vectors in terms of both magnitude and phase for each antenna. The code effectively demonstrates the application of LSTM networks in wireless scenarios to predict complex parameters, and it can be adapted to utilize real-world or measured datasets.