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Performance analysis of multi user massive MIMO hybrid beamforming systems at millimeter wave frequency bands

https://doi.org/10.1007/s11276-021-02546-w
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/s11432-020-2937-yopenalex-snapshot
Multitask deep learning-based multiuser hybrid beamforming for mm-wave orthogonal frequency division multiple access systems
inferredcites →https://doi.org/10.1109/access.2019.2949786openalex-snapshot
Optimal Hybrid Beamforming Design for Millimeter-Wave Massive Multi-User MIMO Relay Systems
inferredcites →https://doi.org/10.1109/glocom.2014.7037444openalex-snapshot
Beamforming for multiuser massive MIMO systems: Digital versus hybrid analog-digital
inferredcites →https://doi.org/10.1109/icc.2016.7510845openalex-snapshot
Hybrid beamforming design for millimeter-wave multi-user massive MIMO downlink
inferredcites →https://doi.org/10.1109/iccchina.2015.7448664openalex-snapshot
Hybrid analog-digital beamforming for multiuser MIMO millimeter wave relay systems
inferredcites →https://doi.org/10.1109/icimia48430.2020.9074973openalex-snapshot
Analysis of 5G Wireless Systems in FR1 and FR2 Frequency Bands
inferredcites →https://doi.org/10.1109/icsipa45851.2019.8977776openalex-snapshot
Multi-user mmWave MIMO Channel Estimation with Hybrid Beamforming over Frequency Selective Fading Channels
inferredcites →https://doi.org/10.1109/jsac.2017.2725682openalex-snapshot
A New View of Multi-User Hybrid Massive MIMO: Non-Orthogonal Angle Division Multiple Access
inferredcites →https://doi.org/10.1109/jstsp.2018.2819130openalex-snapshot
Channel Estimation and Hybrid Precoding for Frequency Selective Multiuser mmWave MIMO Systems
inferredcites →https://doi.org/10.1109/jstsp.2018.2824246openalex-snapshot
Spectral Efficiency Optimization For Millimeter Wave Multiuser MIMO Systems
inferredcites →https://doi.org/10.1109/lcomm.2016.2542161openalex-snapshot
Robust and Low Complexity Hybrid Beamforming for Uplink Multiuser MmWave MIMO Systems
inferredcites →https://doi.org/10.1109/lwc.2014.2363831openalex-snapshot
Low-Complexity Hybrid Precoding in Massive Multiuser MIMO Systems
inferredcites →https://doi.org/10.1109/lwc.2018.2878766openalex-snapshot
Optimal Hybrid Beamforming for Multiuser Massive MIMO Systems With Individual SINR Constraints
inferredcites →https://doi.org/10.1109/lwc.2018.2886882openalex-snapshot
Hybrid Beamforming for Millimeter Wave Massive Multiuser MIMO Systems Using Regularized Channel Diagonalization
inferredcites →https://doi.org/10.1109/lwc.2019.2906168openalex-snapshot
Blind Multiuser Detection in Massive MIMO Channels With Clustered Sparsity
inferredcites →https://doi.org/10.1109/lwc.2019.2943466openalex-snapshot
Beamforming Design for Large-Scale Antenna Arrays Using Deep Learning
inferredcites →https://doi.org/10.1109/mcom.2014.6979962openalex-snapshot
Mimo for millimeter-wave wireless communications: beamforming, spatial multiplexing, or both?
inferredcites →https://doi.org/10.1109/mcom.2017.1600400openalex-snapshot
Hybrid Beamforming for Massive MIMO: A Survey
inferredcites →https://doi.org/10.1109/tcomm.2013.020413.110848openalex-snapshot
Energy and Spectral Efficiency of Very Large Multiuser MIMO Systems
inferredcites →https://doi.org/10.1109/tcomm.2018.2829511openalex-snapshot
Hybrid Beamforming for Multi-User Massive MIMO Systems
inferredcites →https://doi.org/10.1109/tcomm.2018.2854188openalex-snapshot
Channel Estimation Strategies for Multi-User mm Wave Systems
inferredcites →https://doi.org/10.1109/tcomm.2019.2893632openalex-snapshot
Hybrid Beamforming for Millimeter Wave Systems Using the MMSE Criterion
inferredcites →https://doi.org/10.1109/tsp.2014.2324991openalex-snapshot
Distributed Compressive CSIT Estimation and Feedback for FDD Multi-User Massive MIMO Systems
inferredcites →https://doi.org/10.1109/tsp.2018.2838557openalex-snapshot
Hybrid Beamforming With Selection for Multiuser Massive MIMO Systems
inferredcites →https://doi.org/10.1109/tvt.2018.2854735openalex-snapshot
Time-Varying Channel Estimation for Millimeter Wave Multiuser MIMO Systems
inferredcites →https://doi.org/10.1109/tvt.2020.2966122openalex-snapshot
Hybrid Beamforming for Multi-User Millimeter-Wave Networks
inferredcites →https://doi.org/10.1109/twc.2014.011714.130846openalex-snapshot
Spatially Sparse Precoding in Millimeter Wave MIMO Systems
inferredcites →https://doi.org/10.1109/twc.2015.2488634openalex-snapshot
Massive MIMO for Maximal Spectral Efficiency: How Many Users and Pilots Should Be Allocated?
inferredcites →https://doi.org/10.1109/twc.2018.2803180openalex-snapshot
Multi-User Millimeter Wave MIMO With Full-Dimensional Lens Antenna Array
inferredcites →https://doi.org/10.1109/twc.2018.2819653openalex-snapshot
Joint Optimization of Computation and Communication Power in Multi-User Massive MIMO Systems
inferredcites →https://doi.org/10.1155/2016/4341068openalex-snapshot
Joint User Scheduling and MU-MIMO Hybrid Beamforming Algorithm for mmWave FDMA Massive MIMO System
inferredcites →https://doi.org/10.1155/2019/9764958openalex-snapshot
Downlink Multiuser Hybrid Beamforming for MmWave Massive MIMO-NOMA System with Imperfect CSI
inferredcites →https://doi.org/10.1561/2000000093openalex-snapshot
Massive MIMO Networks: Spectral, Energy, and Hardware Efficiency
inferredcites →https://doi.org/10.17993/3ctecno.2019.specialissue.09openalex-snapshot
An optimized hybrid beamforming for millimeter wave musassive MIMO system
inferredcites →https://doi.org/10.3390/electronics9020301openalex-snapshot
Low-Complexity Joint Channel Estimation for Multi-User mmWave Massive MIMO Systems

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Stbc-Based Hybrid Precoding with Dft Codebook Construction in Massive Mimo Systems
inferred← citeshttps://doi.org/10.1002/dac.5743openalex-snapshot
Energy‐efficient hybrid beamforming for millimeter‐wave‐based massive multiple‐input multiple‐output system
inferred← citeshttps://doi.org/10.1002/itl2.569openalex-snapshot
Innovative resource allocation mechanism for optimizing 5G multi user ‐massive multiple input multiple output system
inferred← citeshttps://doi.org/10.1007/978-981-16-7610-9_73openalex-snapshot
Hybrid Beamforming for Massive MIMO Antennas Under 6 GHz Mid-Band
inferred← citeshttps://doi.org/10.1007/978-981-19-2308-1_44openalex-snapshot
RF-MEMS Technology and Beamforming in 5G: Challenges and Opportunities for a Pair with a Still Untapped Potential
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An Energy Efficient Secure Optimal Secrecy Sum Rate Maximization Mechanism for MIMO-NOMA Uplink Transmission in 6G Networks Using a Heuristic Approach
inferred← citeshttps://doi.org/10.1007/s10776-021-00546-3openalex-snapshot
Design and Feasibility Verification of 6G Wireless Communication Systems with State of the Art Technologies
inferred← citeshttps://doi.org/10.1007/s11235-023-01065-7openalex-snapshot
Energy efficient phase interpolator based hybrid beamforming architecture for massive MIMO system
inferred← citeshttps://doi.org/10.1007/s11235-024-01246-yopenalex-snapshot
Optimized beamforming for multiuser massive MIMO 6G wireless networks
inferred← citeshttps://doi.org/10.1007/s11276-023-03380-yopenalex-snapshot
An optimization-driven model for a hybrid beam forming for massive multiple-input multiple-output
inferred← citeshttps://doi.org/10.1007/s11276-024-03809-yopenalex-snapshot
Adaptive connected hybrid beamforming for energy efficiency maximization in multi-user millimeter wave systems
inferred← citeshttps://doi.org/10.1007/s11277-022-09952-zopenalex-snapshot
Hybrid Beamforming in 5G NR Networks Using Multi User Massive MIMO at FR2 Frequency Bands
inferred← citeshttps://doi.org/10.1007/s11277-026-12166-2openalex-snapshot
A FPGA-Enabled Hybrid Beamformer for 5G Millimeter Wave Massive MIMO System
inferred← citeshttps://doi.org/10.1007/s12046-026-03322-2openalex-snapshot
Space-time block coding-based hybrid precoding in massive MIMO systems
inferred← citeshttps://doi.org/10.1007/s12243-024-01013-9openalex-snapshot
Orthogonal beamforming technique for massive MIMO systems
inferred← citeshttps://doi.org/10.1007/s40031-024-01131-xopenalex-snapshot
Deep Learning-Based Channel Estimation and Beamforming Architecture for Massive MIMO Systems
inferred← citeshttps://doi.org/10.1007/s41870-023-01168-zopenalex-snapshot
Interference reduction in hybrid beamforming using 2-D overlapped partially connected subarray structure
inferred← citeshttps://doi.org/10.1007/s41870-024-01946-3openalex-snapshot
Experimental evaluation of MU-MIMO in TDD environment for 5G NR using exploiting channel reciprocity
inferred← citeshttps://doi.org/10.1016/j.aeue.2023.154971openalex-snapshot
Accurate channel estimation and hybrid beamforming using Artificial Intelligence for massive MIMO 5G systems
inferred← citeshttps://doi.org/10.1016/j.asej.2025.103752openalex-snapshot
Residual Bi-RNN driven adaptive deep learning-optimization for the joint power allocation and signal detection in a secure MIMO-NOMA system
inferred← citeshttps://doi.org/10.1016/j.knosys.2022.109993openalex-snapshot
Evolutionary gravitational Neocognitron neural network based differential spatial modulation detection scheme for uplink multiple user huge MIMO systems
inferred← citeshttps://doi.org/10.1016/j.phycom.2022.101817openalex-snapshot
Two-level-enforced security with hybrid precoding for multicast massive MIMO wiretap systems
inferred← citeshttps://doi.org/10.1016/j.rineng.2024.103209openalex-snapshot
Super low profile 5G mmWave highly isolated MIMO antenna with 360∘ pattern diversity for smart city IoT and vehicular communication
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inferred← citeshttps://doi.org/10.1109/globecom54140.2023.10437857openalex-snapshot
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Neural Network-based Subcarrier-level Joint Channel Estimation and Decoding for MIMO-OFDM Receivers
inferred← citeshttps://doi.org/10.1109/tvt.2023.3338065openalex-snapshot
Robust Beamforming for Massive MIMO LEO Satellite Communications: A Risk-Aware Learning Framework
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Performance Analysis of a Downlink mmWave Communication in an IRS-Based Large-scale MIMO Networks
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Hybrid Beamforming for Millimeter Wave Massive MIMO under Multicell Multiuser Environment
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A Deep Learning Framework for Adaptive Beamforming in Massive MIMO Millimeter Wave 5G Multicellular Networks
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Efficient data transmission on wireless communication through a privacy-enhanced blockchain process
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