Joint Tx-Rx beamforming design for multicarrier MIMO channels: a unified framework for convex optimization

By D. P. Palomar, John M. Cioffi, Miguel Angel Lagunas. IEEE Transactions on Signal Processing, 2003

1,192 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/5d9aa20117f52b4e8b4704f882d1e3acbc1c2792

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In Computer Networks and Communications

In Electrical and Electronic Engineering

Citations in the corpus

Frequency Selective Hybrid Precoding for Limited Feedback Millimeter Wave Systems (2015, methodology): “Initial work was focused on deriving optimum precoders and combiners under different criteria [34]–[36].”
Linear Propagation Effects in Mode-Division Multiplexing Systems (2014, background): “3], which finds application in the Google Pagerank algorithm [49] and beamforming [50].”
Joint transceiver design for MIMO communications using geometric mean decomposition (2005, methodology): “The MTM scheme, or ARITH-MSE, which has appeared in several linear transceiver design papers (see, e.g., [15], [16], and [19]), attempts to minimize Tr with respect to .”
Variable-phase-shift-based RF-baseband codesign for MIMO antenna selection (2005, background): “From a formal point of view, preprocessing for antenna selection can also be viewed as a special case of linear precoder designs [29], [30] or, more generally, precoder-decoder joint designs; see, e.g., [31]–[ 34 ].”
Performance Analysis of ZF and MMSE Equalizers for MIMO Systems: An In-Depth Study of the High SNR Regime (2011, neutral): “, [4], [5], and the references therein).”
A robust maximin approach for MIMO communications with imperfect channel state information based on convex optimization (2006, methodology): “In some works such as [ 6 ] and [7], and references therein, the design is done according to several performance criteria and using the tools and the potential provided by convex optimization theory [8].”
Spatially Sparse Precoding in Millimeter Wave MIMO Systems (2013, background): “Unfortunately, finding global optima for similar constrained joint optimization problems is often found to be intractable [64], [65].”
Hybrid Digital and Analog Beamforming Design for Large-Scale Antenna Arrays (2016, background): “However, the joint transmitter-receive matrix design, for similarly constrained optimization problem is usually found to be difﬁ-cult to solve [40].”
Linear precoding via conic optimization for fixed MIMO receivers (2006, background): “One of the interesting properties of these joint desig n is that maximizing the signal-to-interference-plus-nois e-ratios (SINR) is related to minimizing the MSE [16].”
Practical algorithms for a family of waterfilling solutions (2005, methodology): “Recently, the problem of joint transmit-receive design to achieve minimum average BER in MIMO channels (with equal constellations) has been solved independently in [7] and [18], obtaining a solution that includes the same waterfilling structure as in the minimization of the trace of the MSE matrix.”
MIMO radar waveform design based on mutual information and minimum mean-square error estimation (2007, background): “or [44] for the basic concepts and some important results, or to [45] for a complete reference on the subject.”
A Unified Framework for Optimizing Linear Nonregenerative Multicarrier MIMO Relay Communication Systems (2009, methodology): “After the establishment of the unified framework, we revisit (3) to derive the optimal structure of and for subcarrier-cooperative systems.”
A tutorial on geometric programming (2007, background): “Applications of geometric programming in other fields include: • chemical engineering [39, 132, 133, 152] • environment quality control [143, 65] • resource allocation in communication and network systems [14, 28, 29, 30, 31, 55, 65, 81, 83, 117] • information theory [11, 12, 84, 90, 29] • probabili”
MIMO Transceiver Design via Majorization Theory (2007, methodology): “From the previous unifying framework based on majorization theory, it follows that the minimization of the BER averaged over the substreams, considered in [36, 111], is a Schur-convex function, provided that the constellations used on the substreams are equal, and therefore it can be optimally solve”
Gradient of mutual information in linear vector Gaussian channels (2006, methodology): “…vector MMSE estimator is given by the MMSE matrix, i.e., the covariance of the estimation error vector (12) An alternative limited description of the performance, which is sometimes useful, is given by the mean-squared norm of the estimation error , which is equal to the trace of the MMSE matrix.”