id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
fuelectenerg-13681	Mohamed, Mahmoud; Aljuaid, Fayez; Koubeisi, Mhd Walid	COMPARATIVE ANALYSIS OF DEEP LEARNING AND TRADITIONAL OPTIMIZATION ALGORITHMS FOR ADAPTIVE BEAMFORMING IN MIMO SYSTEMS	2025	21	.pdf	application/pdf	8649	491	47	This study provides a comprehensive methodological contribution through rigorous comparative analysis between deep learning approaches and traditional optimization algorithms for adaptive beamforming in MIMO systems. Our findings reveal that deep learning approaches achieve significantly faster convergence (23.7%, p < 0.01) and higher SINR (18.5%, p < 0.01) in dynamic channel conditions, while traditional algorithms maintain superior performance in steady-state scenarios.	cache/fuelectenerg-13681.pdf	txt/fuelectenerg-13681.txt