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Time-Stepped LSTM Framework for 5G Beamforming Vector Prediction

Citation Author(s):
Ardavan Rahimian
Submitted by:
Ardavan Rahimian
Last updated:
DOI:
10.21227/7zzs-b612
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Average: 5 (1 vote)

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