Transmit beamforming for physical-layer multicasting

By N. Sidiropoulos, Timothy N. Davidson, Zhi-Quan Tom Luo. IEEE Transactions on Signal Processing, 2006

1,346 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/83ab3005a9bc9260ac8c4648840f63aafa8f6e4d

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In Computer Networks and Communications

In Electrical and Electronic Engineering

Citations in the corpus

Symbiotic Radio: A New Communication Paradigm for Passive Internet of Things (2018, methodology): “Otherwise, we use the randomization-based method [41] to obtain an approximate (suboptimal) solution to (P1).”
Convex Optimization-Based Beamforming (2010, background): “Notice that this relaxation “restores” up to full covariance rank, yielding the capacity-optimal transmit covariance [24], [59] [cf.”
Distributed Beamforming for Relay Networks Based on Second-Order Statistics of the Channel State Information (2008, methodology): “For the cases where the SDP problem has a solution with rank higher than one, several randomization techniques have been proposed in the literature which use the solution to the SDP problem to provide a good approximation to the rank-1 problem [21].”
QoS-Based Transmit Beamforming in the Presence of Eavesdroppers: An Optimized Artificial-Noise-Aided Approach (2011, background): “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].”
Exploiting Known Interference as Green Signal Power for Downlink Beamforming Optimization (2015, background): “Note that different from the classical multicast beamforming design, which is non-convex and difficult to solve [44], (16) is a convex problem with a quadratic objective function and”
Principles of minimum variance robust adaptive beamforming design (2013, background): “The paper [2] overviews the recent applications of adaptive and robust beamforming to new emerging fields in wireless communications such as downlink beamforming in cellular wireless networks [29], robust code-division multiple-access (CDMA) multiuser detection [30–32], linear receiver design for mu”
Quality of Service and Max-Min Fair Transmit Beamforming to Multiple Cochannel Multicast Groups (2008, background): “This is not acceptable in multicasting applications, because it is the worst SNR that determines the common information rate.”
Learning Optimal Resource Allocations in Wireless Systems (2018, background): “Problems of this form range from the simple power allocation in wireless fading channels – the solution of which is given by water ﬁlling – to the optimization of frequency division multiplexing [1], beamforming [2], [3], and random access [4], [5].”
What Will 5G Be? (2014, neutral): “massification of MIMO multicasting [98], [99].”
Sum-Rate Maximization for Linearly Precoded Downlink Multiuser MISO Systems With Partial CSIT: A Rate-Splitting Approach (2016, background): “In fac t, deterministic subproblems of (10) are known to be non-conve x and non-trivial [14], [35].”
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].”
Content-Centric Sparse Multicast Beamforming for Cache-Enabled Cloud RAN (2015, background): “Compared with point-to-point unicast transmission, pointto-multipoint multicast transmission provides an efficient capacity-offloading approach for common content delivery to multiple subscribers on a same resource block [6], [7].”
Zero-Forcing Precoding and Generalized Inverses (2008, background): “is an increasing interest in analyzing the tightness of such relaxations [24], [25].”
Learning to Optimize: Training Deep Neural Networks for Interference Management (2017, background): “Semidefinite relaxation (SDR) based schemes [7], [8] first lift the original vector-based optimization problems into matrix-based problems, and then solve one or a few semidefinite programs (each of which is in turn solved by dedicated iterative algorithms).”
Quantized Precoding for Massive MU-MIMO (2016, methodology): “o ﬁnd near-ML solutions for the MU-MIMO detection problem (see, e.g., [58], [59]). For the case when the BS is equipped with inﬁnite-resolution DACs, SDR has been used for downlink precoding in [60], [61]. We next show how SDR can be used to ﬁnd approximate solutions to (35). In our context, SDR inv”
Federated Learning via Over-the-Air Computation (2018, positive): “A natural way to address it is adopting the matrix lifting technique [40].”
Intelligent Reflecting Surface Aided Multigroup Multicast MISO Communication Systems (2019, methodology): “A common performance metric in multicast transmissions is the max-min fairness (MMF), where the minimum signal-tointerference-plus-noise-ratio (SINR) or spectral efficiency of users in each multicasting group or among all multicasting groups is maximized [16]–[20].”