On downlink beamforming with greedy user selection: performance analysis and a simple new algorithm

By Goran Dimic, N. Sidiropoulos. IEEE Transactions on Signal Processing, 2005

635 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/52944c847cd67440a50157aa259aa246b8c06d83

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In Computer Networks and Communications

In Electrical and Electronic Engineering

Citations in the corpus

Shifting the MIMO Paradigm (2007, methodology): “Scheduling for the nonlinear precoders mentioned previously is an ongoing topic of research, though few results have appeared, including a greedy user selection for ZF DPC (ZFDPC), which has been proposed in [27].”
Joint Transmit Beamforming for Multiuser MIMO Communications and MIMO Radar (2019, methodology): “ZF methods facilitate obtaining closed-form, tractable, and interpretable precoders [58], [69].”
MIMO Relaying With Linear Processing for Multiuser Transmission in Fixed Relay Networks (2008, methodology): “The algorithm proposed here is a modification to the algorithm in [33] and [ 40 ].”
Joint Spatial Division and Multiplexing: Opportunistic Beamforming, User Grouping and Simplified Downlink Scheduling (2014, methodology): “the performance of ZFBF with random user selection (denoted by ZFBF-RANDOM-US), ZFBF with greedy user selection [35] (denoted by ZFBF-GUS) and ZFBF with semi-orthogonal user selection [19] (denoted by ZFBF-SUS), where instead of restricting to JSDM with per-group processing, the selection is perform”
Zero-Forcing Precoding and Generalized Inverses (2008, background): “Roughly speaking, we will show that the optimization of depends on the design criteria (fairness versus throughput), whereas the optimization of is associated with the power constraints (total versus per-antenna).”
Massive MIMO Systems for 5G and beyond Networks—Overview, Recent Trends, Challenges, and Future Research Direction (2020, background): “Several user scheduling algorithms have been proposed to improve the sum capacity, but computational complexity was not improved for a large number of antennas [107,108].”
Multiuser MIMO Achievable Rates With Downlink Training and Channel State Feedback (2007, methodology): “Simulation results show that a system with K = 10 and M = 4, with a greedy scheduling as proposed in [ 15 ], [32], achieves a very small gap with respect to the optimal dirty-paper coding and perfect”