id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
fuelectenerg-12001	Samantaray, Barsa; Das, Kunal Kumar; Roy, Jibendu Sekhar	COMPARISON OF THE PERFORMANCE OF ARTIFICIAL NEURAL NETWORK WITH VARIABLE STEP-SIZE ADAPTIVE ALGORITHMS FOR THE BEAMFORMING OF SMART ANTENNA FOR CELLULAR NETWORKS	2024	11	.pdf	application/pdf	3909	231	67	dB VS-SLMS 15° 14.5° 30° 29.5° -8.69 dB VS-LMS 15° 15.4° 30° 27.6° -9.15 dB N=16 ANN 10° 10.1° 22° 22.1° -13.05 dB VS-SLMS 10° 10.4° 22° 22.3° -8.72 dB VS-LMS 10° 9.6° 22° 21.6° -9.40 dB N=20 ANN 20° 20.0° 32° 32.0° -13.40 dB VS-SLMS 20° 19.0° 32° 31.8° -9.88 dB VS-LMS 20° 19.6° 32° 26.5° -9.00 dB In Table 1, the deviations of BD and ND from the desired values are less for ANN than for VS-LMS and VS-SLMS. [25] Recurrent neural network (RNN) ULA with N=32, d=λ/2, BD=00 -7.5 dB Ref [25] RNN ULA with N=16, d=λ/2, BD=-100 -8.5 dB Ref [27] RNN based on the gated recurrent unit ULA with N=16, d=λ/2, BD=1000 -11.5 dB Ref [35] Elman RNN ULA with N=5, d=λ/2, BD=300 -11.5 dB This paper ANN ULA with N=10, d=λ/2, BD=00 -13.3 dB This paper ANN ULA with N=16, d=λ/2, BD=100 -13.05 dB This paper ANN ULA with N=20, d=λ/2, BD=200 -13.4 dB One of the main sources of interference in a cellular network is the side lobes of the desired radiation beam.	cache/fuelectenerg-12001.pdf	txt/fuelectenerg-12001.txt