Time-Stepped LSTM Framework for 5G Beamforming Vector Prediction

- Citation Author(s):
- Submitted by:
- Ardavan Rahimian
- Last updated:
- DOI:
- 10.21227/7zzs-b612
- Categories:
- Keywords:
Abstract
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.
Instructions:
To run this code, save the file as 'STS_BFN.m' and execute in MATLAB by typing: >> STS_BFN