Time-Stepped LSTM Framework for 5G Beamforming Vector Prediction

This MATLAB script presents an innovative approach to 5G beamforming prediction using a sequence-based LSTM neural network. Unlike conventional methods that predict only final vectors, this solution provides time-stepped predictions across entire sequences, enabling real-time tracking of dynamic channel conditions. The framework achieves stable training convergence while maintaining physically meaningful performance metrics, including realistic path loss and SNR values. Its modular design serves as a foundation for easily integrating more sophisticated channel models and beamforming algorithms, making it particularly valuable for 5G and beyond system modeling and optimization.