Quality of Service and Max-Min Fair Transmit Beamforming to Multiple Cochannel Multicast Groups

By Eleftherios Karipidis, N. Sidiropoulos, Zhi-Quan Tom Luo. IEEE Transactions on Signal Processing, 2008

564 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/3efae82c2da74f83bc7e3c2a8e570e8d551cd766

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In Computer Networks and Communications

In Electrical and Electronic Engineering

Citations in the corpus

Optimal Resource Allocation in Coordinated Multi-Cell Systems (2013, background): “Multi-cast signals can, for example, be used for overhead signaling to different groups of users [127, 245].”
Parallel and Distributed Methods for Constrained Nonconvex Optimization—Part I: Theory (2016, methodology): “We study the general Max-Min Fair (MMF) beamforming problem for multi -group multicasting [22], where different groups of subscribers request different data streams from the transmitter; see Fig.”
QoS-Based Transmit Beamforming in the Presence of Eavesdroppers: An Optimized Artificial-Noise-Aided Approach (2011, methodology): “2094610 QoS-based transmit beamforming has proven to be a viable, versatile approach to a variety of communication scenarios, such as downlink unicast [2]–[5], downlink multicast [6], and multi-group multicast (a combination of unicast and multicast) [7].”
Content-Centric Sparse Multicast Beamforming for Cache-Enabled Cloud RAN (2015, methodology): “Thus we can claim that our proposed G-CCP based sparse mutic ast beamforming algorithm is general and can be used to solve the traditional multi-group multicast beamforming problem [25], [28].”
Convex Optimization-Based Beamforming (2010, background): “[62], [65], [66], and cross-layer multicast beamforming and admission control in [62].”
Outage Constrained Robust Transmit Optimization for Multiuser MISO Downlinks: Tractable Approximations by Conic Optimization (2011, unknown): “This issue has also been identified before in the context of multigroup multicast beamforming with perfect CSI [27].”
Intelligent Reflecting Surface Aided Multigroup Multicast MISO Communication Systems (2019, methodology): “y of users in each multicasting group or among all multicasting groups is maximized [16]–[20]. Prior seminal treatments of multicast transmission in single-group and multigroup are presented in [16], [17], where the MMF problems are formulated as a fractional second-order cone programming (SOCP) and”