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Daniel P. Palomar
Researcher at Hong Kong University of Science and Technology
Publications - 297
Citations - 19202
Daniel P. Palomar is an academic researcher from Hong Kong University of Science and Technology. The author has contributed to research in topics: MIMO & Convex optimization. The author has an hindex of 61, co-authored 287 publications receiving 16892 citations. Previous affiliations of Daniel P. Palomar include Polytechnic University of Catalonia & Hong Kong Baptist University.
Papers
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A tutorial on decomposition methods for network utility maximization
Daniel P. Palomar,Mung Chiang +1 more
TL;DR: This tutorial paper first reviews the basics of convexity, Lagrange duality, distributed subgradient method, Jacobi and Gauss-Seidel iterations, and implication of different time scales of variable updates, and introduces primal, dual, indirect, partial, and hierarchical decompositions, focusing on network utility maximization problem formulations.
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Joint Tx-Rx beamforming design for multicarrier MIMO channels: a unified framework for convex optimization
TL;DR: This paper addresses the joint design of transmit and receive beamforming or linear processing for multicarrier multiple-input multiple-output (MIMO) channels under a variety of design criteria by developing a unified framework based on considering two families of objective functions that embrace most reasonable criteria to design a communication system.
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Majorization-Minimization Algorithms in Signal Processing, Communications, and Machine Learning
TL;DR: An overview of the majorization-minimization (MM) algorithmic framework, which can provide guidance in deriving problem-driven algorithms with low computational cost and is elaborated by a wide range of applications in signal processing, communications, and machine learning.
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Power Control By Geometric Programming
TL;DR: This work presents a systematic method of distributed algorithms for power control that is geometric-programming-based and shows that in the high Signal-to- interference Ratios (SIR) regime, these nonlinear and apparently difficult, nonconvex optimization problems can be transformed into convex optimized problems in the form of geometric programming.
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Rank-Constrained Separable Semidefinite Programming With Applications to Optimal Beamforming
Yongwei Huang,Daniel P. Palomar +1 more
TL;DR: Conditions under which strong duality holds and efficient algorithms for the optimal beamforming problem are given and rank reduction procedures to achieve a lower rank solution are proposed.