258 citations · 1.2k across the 44 of their papers we have counts for
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A Family of Deep Learning Architectures for Channel Estimation and Hybrid Beamforming in Multi-Carrier mm-Wave Massive MIMO
Ahmet M. Elbir, Kumar Vijay Mishra, M. R. Bhavani Shankar +1
Hybrid analog and digital beamforming transceivers are instrumental in addressing the challenge of expensive hardware and high training overheads in the next generation millimeter-…
DeepMUSIC: Multiple Signal Classification via Deep Learning
Ahmet M. Elbir
This letter introduces a deep learning (DL) framework for direction-of-arrival (DOA) estimation. Previous works in DL context mostly consider a single or two target scenario which…
V-Shaped Sparse Arrays For 2-D DOA Estimation
Ahmet M. Elbir
This paper proposes a new sparse array geometry for 2-D (azimuth and elevation) DOA (direction-of-arrival) estimation. The proposed array geometry is V-shaped sparse array and it i…
Hybrid Precoding for Multi-User Millimeter Wave Massive MIMO Systems: A Deep Learning Approach
Ahmet M. Elbir, Anastasios Papazafeiropoulos
In multi-user millimeter wave (mmWave) multiple-input-multiple-output (MIMO) systems, hybrid precoding is a crucial task to lower the complexity and cost while achieving a sufficie…
Low-Complexity Limited-Feedback Deep Hybrid Beamforming for Broadband Massive MIMO Communications
Ahmet M. Elbir, Kumar Vijay Mishra
In broadband millimeter-wave (mm-Wave) systems, it is desirable to design hybrid beamformers with common analog beamformer for the entire band while employing different baseband be…
Joint Antenna Selection and Hybrid Beamformer Design using Unquantized and Quantized Deep Learning Networks
Ahmet M. Elbir, Kumar Vijay Mishra
In millimeter-wave communications, multiple-input-multiple-output (MIMO) systems use large antenna arrays to achieve high gain and spectral efficiency. These massive MIMO systems e…