Publications

Books and Monographs

• Daniel P. Palomar, Portfolio Optimization: From Theory to Practive, Cambridge University Press, 2024.

• Konstantinos Benidis, Yiyong Feng, and Daniel P. Palomar, Optimization Methods for Financial Index Tracking: From Theory to Practice, Foundations and Trends® in Optimization, Now Publishers, 2018. 📔

• Yiyong Feng and Daniel P. Palomar, A Signal Processing Perspective on Financial Engineering, Foundations and Trends® in Signal Processing, Now Publishers, 2016. 📕

• Daniel P. Palomar and Yonina C. Eldar, Eds., Convex Optimization in Signal Processing and Communications, Cambridge University Press, 2009. 📕

• Daniel P. Palomar and Yi Jiang, MIMO Transceiver Design via Majorization Theory, Foundations and Trends® in Communications and Information Theory, Now Publishers, vol. 3, no. 4-5, 2007. 📘 🚧

Book Chapters

• José Vinícius de M. Cardoso, Jiaxi Ying, and Daniel P. Palomar, “Learning Graphs from Heavy-Tailed Data,” in Elliptically Symmetric Distributions in Signal Processing and Machine Learning, Eds. J.-P. Delmas, M. N. El Korso, S. Fortunati, F. Pascal, Springer, Jul. 2024.

• José Vinícius de M. Cardoso, Jiaxi Ying, and Daniel P. Palomar, “Nonconvex Graph Learning: Sparsity, Heavy-tails, and Clustering,” in Signal Processing and Machine Learning Theory, Digital Signal Processing Series, Elsevier, Dec. 2022. 📕

• Linlong Wu and Daniel P. Palomar, “Radar Waveform Design via the Majorization-Minimization Framework,” in Radar Waveform Design Based on Optimization Theory, 2020. 📕

• Gesualdo Scutari, Daniel P. Palomar, Francisco Facchinei, and Jong-Shi Pang, “Monotone Games for Cognitive Radio Systems,” in Distributed Decision-Making and Control, Ch. 4, Eds. Anders Rantzer and Rolf Johansson, Lecture Notes in Control and Information Sciences Series, Springer Verlag, 2011. 📕

• Jiaheng Wang and Daniel P. Palomar, “Majorization Theory with Applications in Signal Processing and Communication Systems,” in Mathematical Foundations for Signal Processing, Communications and Networking, Ch. 16, Eds. Thomas Chen, Dinesh Rajan, and Erchin Serpedin, CRC Press, 2011. 📕

• Gesualdo Scutari, Daniel P. Palomar, and Sergio Barbarossa, “Competitive Optimization of Cognitive Radio MIMO Systems via Game Theory,” in Convex Optimization in Signal Processing and Communications, Cambridge Univ. Press, 2009. 📕

• Mung Chiang, Chee Wei Tan, Daniel P. Palomar, Daniel O’Neill, and David Julian, “Power Control by Geometric Programming,” in Resource Allocation in Next Generation Wireless Networks, vol. 5, Chapter 13, pp. 289-313, W. Li, Y. Pan, Editors, Nova Sciences Publishers, ISBN 1-59554-583-9, 2005. 📕

• Daniel P. Palomar, A. Pascual-Iserte, John M. Cioffi, and Miguel A. Lagunas, “Convex Optimization Theory Applied to Joint Transmitter-Receiver Design in MIMO Channels,” in Space-Time Processing for MIMO Communications, Chapter 8, pp. 269-318, A. B. Gershman and N. Sidiropoulos, Editors, John Wiley & Sons, ISBN 0-470-01002-9, April 2005. 📕

• Daniel P. Palomar, “Unified Design of Linear Transceivers for MIMO Channels,” in Smart Antennas – State-of-the-Art, vol. 3, Chapter 18, EURASIP Hindawi Book Series on SP&C, T. Kaiser, A. Bourdoux, H. Boche, J. R. Fonollosa, J. B. Andersen, and W. Utschick, Editors, ISBN 977-5945-09-7, 2005. 📕

Journal Papers ¹

2024

• Runhao Shi and Daniel P. Palomar, “SAOFTRL: A Novel Adaptive Algorithmic Framework for Enhancing Online Portfolio Selection,” IEEE Trans. on Signal Processing, vol. –, pp. -, 2024.

• Runhao Shi, Jiaxi Ying, and and Daniel P. Palomar, “Adaptive Passive-Aggressive Framework for Online Regression with Side Information,” Advances in Neural Information Processing Systems (NeurIPS), Vancouver, Canada, Dec. 2024. [25.8% acceptance rate]

• Syed Awais W. Shah, Daniel P. Palomar, Ian Barr, Leo L. M. Poon, Ahmed Abdul Quadeer, and Matthew R. McKay, “Seasonal antigenic prediction of influenza A H3N2 using machine learning,” Nature Communications, vol. 15, no. 3833, 2024.

• Amirhossein Javaheri, Arash Amini, Farokh Marvasti, and Daniel P. Palomar, “Learning Spatio-Temporal Graphical Models From Incomplete Observations,” IEEE Trans. on Signal Processing, vol. 72, pp. 1361-1374, 2024.

2023

• Jian-Feng Cai, José Vinícius de M. Cardoso, Daniel P. Palomar, and Jiaxi Ying, “Fast Projected Newton-like Method for Precision Matrix Estimation under Total Positivity,” Advances in Neural Information Processing Systems (NeurIPS), New Orleans, LA, USA, Dec. 2023. [26.1% acceptance rate] [video] [slides] [poster]

• Xiwen Wang, Jiaxi Ying, and Daniel P. Palomar, “Learning Large-Scale MTP2 Gaussian Graphical Models via Bridge-Block Decomposition,” Advances in Neural Information Processing Systems (NeurIPS), New Orleans, LA, USA, Dec. 2023. [26.1% acceptance rate] [video] [slides] [poster]

• Xiwen Wang, Rui Zhou, Jiaxi Ying, and Daniel P. Palomar, “Efficient and Scalable Parametric High-Order Portfolios Design via the Skew-t Distribution,” IEEE Trans. on Signal Processing, vol. 71, pp. 3726-3740, 2023.

• Esa Ollila, Daniel P. Palomar, and Frédéric Pascal, “Affine equivariant Tyler’s M-estimator applied to tail parameter learning of elliptical distributions,” IEEE Signal Processing Letters, vol. 30, pp. 1017-1021, Aug. 2023.

• Shengjie Xiu, Xiwen Wang, and Daniel P. Palomar, “A Fast Successive QP Algorithm for General Mean-Variance Portfolio Optimization,” IEEE Trans. on Signal Processing, vol. 71, pp. 2713-2727, July 2023.

• Jiaxi Ying, José Vinícius de M. Cardoso, and Daniel P. Palomar, “Adaptive Estimation of Graphical Models under Total Positivity,” in Proc. of the International Conference on Machine Learning (ICML), Honolulu, HI, USA, July 2023. [27.9% acceptance rate]

2022

• José Vinícius de M. Cardoso, Jiaxi Ying, and Daniel P. Palomar, “Learning Bipartite Graphs: Heavy Tails and Multiple Components,” Advances in Neural Information Processing Systems (NeurIPS), New Orleans, LA, USA, Dec. 2022. [25.6% acceptance rate] [video] [slides] [poster] [R package bipartite]

• Rui Zhou, Jiaxi Ying, and Daniel P. Palomar, “Covariance Matrix Estimation Under Low-Rank Factor Model with Nonnegative Correlations,” IEEE Trans. on Signal Processing, vol. 70, pp. 4020-4030, Aug. 2022.

• Xiwen Wang, Jiaxi Ying, José Vinícius de M. Cardoso, and Daniel P. Palomar, “Efficient Algorithms for General Isotone Optimization,” in The Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI), Feb. 2022. [15% acceptance rate]

2021

• José Vinícius de M. Cardoso, Jiaxi Ying, and Daniel P. Palomar, “Graphical Models for Heavy-Tailed Markets,” Advances in Neural Information Processing Systems (NeurIPS), Virtual, Dec. 2021. [26% acceptance rate] [video] [supplemental material] [slides] [poster] [R package fingraph]

• Arnaud Breloy, Sandeep Kumar, Ying Sun, and Daniel P. Palomar, “Majorization-Minimization on the Stiefel Manifold with Application to Robust Sparse PCA,” IEEE Trans. on Signal Processing, vol. 69, pp. 1507-1520, Feb. 2021.

• Jiaxi Ying, José Vinícius de M. Cardoso, and Daniel P. Palomar, “Minimax Estimation of Laplacian Constrained Precision Matrices,” in Proc. of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS), vol. 130, pp. 3736-3744, April 2021. [29.8% acceptance rate] [R package]

• Rui Zhou and Daniel P. Palomar, “Solving High-Order Portfolios via Successive Convex Approximation Algorithms,” IEEE Trans. on Signal Processing, vol. 69, pp. 892-904, Feb. 2021.

• Esa Ollila, Daniel P. Palomar, and Frédéric Pascal, “Shrinking the Eigenvalues of M-estimators of Covariance Matrix,” IEEE Trans. on Signal Processing, vol. 69, pp. 256-269, Jan. 2021.

2020

• Rui Zhou, Junyan Liu, Sandeep Kumar, and Daniel P. Palomar, “Student’s t VAR Modeling with Missing Data via Stochastic EM and Gibbs Sampling,” IEEE Trans. on Signal Processing, vol. 68, pp. 6198-6211, Oct. 2020.

• Jiaxi Ying, José Vinícius de M. Cardoso, and Daniel P. Palomar, “Nonconvex Sparse Graph Learning under Laplacian Constrained Graphical Model,” Advances in Neural Information Processing Systems (NeurIPS), Vancouver, Canada, Dec. 2020. [20.1% acceptance rate] [2-min video] [slides] [poster] [R package]

• Rui Zhou and Daniel P. Palomar, “Understanding the Quintile Portfolio,” IEEE Trans. on Signal Processing, vol. 68, pp. 4030-4040, July 2020.

• Sandeep Kumar, Jiaxi Ying, José Vinícius de M. Cardoso, and Daniel P. Palomar, “A Unified Framework For Structured Graph Learning Via Spectral Constraints,” Journal of Machine Learning Research (JMLR), 21(22): 1-60, Jan. 2020. [R package spectralGraphTopology]

2019

• Linlong Wu, Yiyong Feng, and Daniel P. Palomar, “General Sparse Risk Parity Portfolio Design via Successive Convex Optimization,” Signal Processing, vol. 170, pp. 1-13, Dec. 2019.

• Sandeep Kumar, Jiaxi Ying, José Vinícius de M. Cardoso, and Daniel P. Palomar, “Structured Graph Learning Via Laplacian Spectral Constraints,” Advances in Neural Information Processing Systems (NeurIPS), Dec. 2019. [2-min video] [slides] [poster] [arXiv] [R package]

• Kaiming Shen, Wei Yu, Licheng Zhao, and Daniel P. Palomar, “Optimization of MIMO Device-to-Device Networks via Matrix Fractional Programming: A Minorization-Maximization Approach,” IEEE/ACM Trans. on Networking, vol. 27, no. 5, pp. 2164-2177, Oct. 2019.

• Linlong Wu and Daniel P. Palomar, “Sequence Design for Spectral Shaping via Minimization of Regularized Spectral Level Ratio,” IEEE Trans. on Signal Processing, vol. 67, no. 18, pp. 4683-4695, Sept. 2019.

• Licheng Zhao, Yiwei Wang, Sandeep Kumar, and Daniel P. Palomar, “Optimization Algorithms for Graph Laplacian Estimation via ADMM and MM,” IEEE Trans. on Signal Processing, vol. 67, no. 16, pp. 4231-4244, Aug. 2019. [R package spectralGraphTopology]

• Junyan Liu and Daniel P. Palomar, “Regularized Robust Estimation of Mean and Covariance Matrix for Incomplete Data,” Signal Processing, vol. 165, pp. 278-291, July 2019.

• Junyan Liu, Sandeep Kumar, and Daniel P. Palomar, “Parameter Estimation of Heavy-Tailed AR Model With Missing Data Via Stochastic EM,” IEEE Trans. Signal Processing, vol. 67, no. 8, pp. 2159-2172, April 2019. [​R package imputeFin]

• Ziping Zhao, Rui Zhou, and Daniel P. Palomar, “Optimal Mean-Reverting Portfolio With Leverage Constraint for Statistical Arbitrage in Finance,” IEEE Trans. on Signal Processing, vol. 67, no. 7, pp. 1681-1695, April 2019.

• Xun Wang, Daniel P. Palomar, Licheng Zhao, Mohamed S. Guidaoui, and Ross Murch, “Spectral-Based Methods for Pipeline Leakage Localization,” Journal of Hydraulic Engineering, 145(3), 2019.

2018

• Licheng Zhao and Daniel P. Palomar, “A Markowitz Portfolio Approach to Options Trading,” IEEE Trans. on Signal Processing, vol. 66, no. 16, pp. 4223-4238, Aug. 2018.
• Ziping Zhao and Daniel P. Palomar, “Mean-Reverting Portfolio With Budget Constraint,” IEEE Trans. on Signal Processing, vol. 66, no. 9, pp. 2342-2357, May 2018.
• Tianyu Qiu, Xiao Fu, Nicholas D. Sidiropoulos, and and Daniel P. Palomar, “MISO Channel Estimation and Tracking from Received Signal Strength Feedback,” IEEE Trans. on Signal Processing, vol. 66, no. 7, pp. 1691-1704, April 2018.
• Linlong Wu, Prabhu Babu, and Daniel P. Palomar, “Transmit Waveform/Receive Filter Design for MIMO Radar With Multiple Sequence Constraints,” IEEE Trans. on Signal Processing, vol. 66, no. 6, pp. 1526-1540, March 2018.
• Konstantinos Benidis, Yiyong Feng, and Daniel P. Palomar, “Sparse Portfolios for High-Dimensional Financial Index Tracking,” IEEE Trans. on Signal Processing, vol. 66, no. 1, pp. 155-170, Jan. 2018. [R package sparseIndexTracking]

2017

• Tianyu Qiu and Daniel P. Palomar, “Undersampled Sparse Phase Retrieval via Majorization-Minimization,” IEEE Trans. on Signal Processing, vol. 65, no. 22, pp. 5957-5969, Nov. 2017.

• Zhongju Wang, Prabhu Babu, and Daniel P. Palomar, “Effective Low-Complexity Optimization Methods for Joint Phase Noise and Channel Estimation in OFDM,” IEEE Trans. on Signal Processing, vol. 65, no. 12, pp. 3247-3260, June 2017.

• Shanpu Shen, Ying Sun, Sichao Song, Daniel P. Palomar, and Ross D. Murch, “Successive Boolean Optimization of Planar Pixel Antennas,” IEEE Trans. on Antennas and Propagation, vol. 65, no. 2, pp. 920-925, Feb. 2017.

• Licheng Zhao and Daniel P. Palomar, “Maximin Joint Optimization of Transmitting Code and Receiving Filter in Radar and Communications,” IEEE Trans. on Signal Processing, vol. 65, no. 4, pp. 850-863, Feb. 2017.

• Ying Sun, Prabhu Babu, and Daniel P. Palomar, “Majorization-Minimization Algorithms in Signal Processing, Communications, and Machine Learning,” IEEE Trans. on Signal Processing, vol. 65, no. 3, pp. 794-816, Feb. 2017.
  🏆 2020 Young Author Best Paper Award by the IEEE Signal Processing Society

• Linlong Wu, Prabu Babu, and Daniel P. Palomar, “Cognitive Radar-Based Sequence Design via SINR Maximization,” IEEE Trans. on Signal Processing, vol. 65, no. 3, pp. 779-793, Feb. 2017.

• Licheng Zhao, Junxiao Song, Prabu Babu, and Daniel P. Palomar, “A Unified Framework for Low Autocorrelation Sequence Design via Majorization-Minimization,” IEEE Trans. on Signal Processing, vol. 65, no. 2, pp. 438-453, Jan. 2017.

• Javier Rubio, Antonio Pascual-Iserte, Daniel P. Palomar, and Andrea Goldsmith, “Joint Optimization of Power and Data Transfer in Multiuser MIMO Systems,” IEEE Trans. on Signal Processing, vol. 65, no. 1, pp. 212-227, Jan. 2017.

2016

• Zhongju Wang, Prabu Babu, and Daniel P. Palomar, “Design of PAR-Constrained Sequences for MIMO Channel Estimation via Majorization-Minimization,” IEEE Trans. on Signal Processing, vol. 64, no. 23, pp. 6132-6144, Dec. 2016.

• Konstantinos Benidis, Ying Sun, Prabhu Babu, and Daniel P. Palomar, “Orthogonal Sparse PCA and Covariance Estimation via Procrustes Reformulation,” IEEE Trans. on Signal Processing, vol. 64, no. 23, pp. 6211-6226, Dec. 2016. [R package sparseEigen] [Matlab code]

• Tianyu Qiu, Prabhu Babu, and Daniel P. Palomar, “PRIME: Phase Retrieval via Majorization-Minimization,” IEEE Trans. on Signal Processing, vol. 64, no. 19, pp. 5174-5186, Oct. 2016.

• Licheng Zhao, Prabhu Babu, and Daniel P. Palomar, “Efficient Algorithms on Robust Low-Rank Matrix Completion Against Outliers,” IEEE Trans. on Signal Processing, vol. 64, no. 18, pp. 4767- 4780, Sept. 2016.

• Yang Yang, Marius Pesavento, Mengyi Zhang, and Daniel P. Palomar, “An Online Parallel Algorithm for Recursive Estimation of Sparse Signals,” IEEE Trans. on Signal and Inform. Proc. Over Networks, vol. 2, no. 3, pp. 290-305, Sept. 2016.

• Maria Gregori, Miquel Payaró, and Daniel P. Palomar, “Sum-Rate Maximization for Energy Harvesting Nodes With a Generalized Power Consumption Model,” IEEE Trans. on Wireless Comm., vol. 15, no. 8, pp. 5341-5354, Aug. 2016.

• Ying Sun, Prabhu Babu, and Daniel P. Palomar, “Robust Estimation of Structured Covariance Matrix for Heavy-Tailed Elliptical Distributions,” IEEE Trans. on Signal Processing, vol. 64, no. 14, pp. 3576-3590, July 2016. [Matlab code]

• Yang Yang, Gesualdo Scutari, Daniel P. Palomar, and Marius Pesavento, “A Parallel Decomposition Method for Nonconvex Stochastic Multi-Agent Optimization Problems,” IEEE Trans. on Signal Processing, vol. 64, no. 11, pp. 2949-2964, June 2016.

• Junxiao Song, Prabhu Babu, and Daniel P. Palomar, “Sequence Set Design With Good Correlation Properties Via Majorization-Minimization,” IEEE Trans. on Signal Processing, vol. 64, no. 11, pp. 2866-2879, June 2016.

• Ying Sun, Arnaud Breloy, Prabhu Babu, Daniel P. Palomar, Frédéric Pascal, and Guillaume Ginolhac, “Low-Complexity Algorithms for Low Rank Clutter Parameter Estimation in Radar Systems,” IEEE Trans. on Signal Processing, vol. 64, no. 8, pp. 1986-1998, April 2016.

• Junxiao Song, Prabhu Babu, and Daniel P. Palomar, “Sequence Design to Minimize the Weighted Integrated and Peak Sidelobe Levels,” IEEE Trans. on Signal Processing, vol. 64, no. 8, pp. 2051-2064, April 2016.

2015

• Yiyong Feng and Daniel P. Palomar, “Normalization of Linear Support Vector Machines,” IEEE Trans. on Signal Processing, vol. 63, no. 17, pp. 4673-4688, Sept. 2015.

• Yiyong Feng and Daniel P. Palomar, “SCRIP: Successive Convex Optimization Methods for Risk Parity Portfolio Design,” IEEE Trans. on Signal Processing, vol. 63, no. 19, pp. 5285-5300, Oct. 2015. [R package riskParityPortfolio] [Python package riskparity.py] [Rust package riskparity.rs]

• Junxiao Song, Prabhu Babu, and Daniel P. Palomar, “Optimization Methods for Designing Sequences With Low Autocorrelation Sidelobes,” IEEE Trans. on Signal Processing, vol. 63, no. 15, pp. 3998-4009, Aug. 2015.

• Ying Sun, Prabhu Babu, and Daniel P. Palomar, “Regularized Robust Estimation of Mean and Covariance Matrix Under Heavy-Tailed Distributions,” IEEE Trans. on Signal Processing, vol. 63, no. 12, pp. 3096-3109, June 2015. [Matlab code] [R package fitHeavyTail]

• Junxiao Song, Prabhu Babu, and Daniel P. Palomar, “Sparse Generalized Eigenvalue Problem via Smooth Optimization,” IEEE Trans. on Signal Processing, vol. 63, no. 7, pp. 1627-1642, April 2015. [Matlab code]

• Yiyong Feng, Daniel P. Palomar, and Francisco Rubio, “Robust Optimization of Order Execution,” IEEE Trans. on Signal Processing, vol. 63, no. 4, pp. 907-920, Feb. 2015.

2014

• Antonio A. D’Amico, Luca Sanguinetti, and Daniel P. Palomar, “Convex Separable Problems with Linear Constraints in Signal Processing and Communications,” IEEE Trans. on Signal Processing, vol. 62, no. 22, pp. 6045-6058, Nov. 2014.

• Ying Sun, Prabhu Babu, and Daniel P. Palomar, “Regularized Tyler’s Scatter Estimator: Existence, Uniqueness, and Algorithms,” IEEE Trans. on Signal Processing, vol. 62, no. 19, pp. 5143-5156, Oct. 2014. [R package fitHeavyTail]

• Gesualdo Scutari, Francisco Facchinei, Jong-Shi Pang, and Daniel P. Palomar, “Real and Complex Monotone Communication Games,” IEEE Trans. on Information Theory, vol. 60, no. 7, pp. 4197-4231, July 2014.

• Italo Atzeni, Luis G. Ordóñez, Gesualdo Scutari, Daniel P. Palomar, and Javier R. Fonollosa, “Noncooperative Day-Ahead Bidding Strategies for Demand-Side Expected Cost Minimization with Real-Time Adjustments: A GNEP Approach,” IEEE Trans. on Signal Processing, vol. 62, no. 9, pp. 2397-2412, May 2014.

• Yongwei Huang and Daniel P. Palomar, “Randomized Algorithms for Optimal Solutions of Double-Sided QCQP with Applications in Signal Processing,” IEEE Trans. on Signal Processing, vol. 62, no. 5, pp. 1093-1108, March 2014.

• Gesualdo Scutari, Francisco Facchinei, Peiran Song, Daniel P. Palomar, and Jong-Shi Pang, “Decomposition by Partial Linearization: Parallel Optimization of Multi-Agent Systems,” IEEE Trans. on Signal Processing, vol. 62, no. 3, pp. 641-656, Feb. 2014.
  🏆 2015 Young Author Best Paper Award by the IEEE Signal Processing Society

• Benjamín Béjar, Santiago Zazo, and Daniel P. Palomar, “Energy Efficient Collaborative Beamforming in Wireless Sensor Networks,” IEEE Trans. on Signal Processing, vol. 62, no. 2, pp. 496-510, Jan. 2014.

2013

• Yang Yang, Francisco Rubio, Gesualdo Scutari, and Daniel P. Palomar, “Multi-Portfolio Optimization: A Potential Game Approach,” IEEE Trans. on Signal Processing, vol. 61, no. 22, pp. 5590-5602, Nov. 2013.

• Xiaopeng Fan, Junxiao Song, Daniel P. Palomar, and Oscar C. Au, “Universal Binary Semidefinite Relaxation for ML Signal Detection,” IEEE Trans. on Communications, vol. 61, no. 11, pp. 4565-4576, Nov. 2013.

• Yang Yang, Gesualdo Scutari, Peiran Song, and Daniel P. Palomar, “Robust MIMO Cognitive Radio Systems Under Interference Temperature Constraints,” IEEE Journal on Selected Areas in Communications, vol. 31, no. 11, pp. 2465-2482, Nov. 2013.

• Mengyi Zhang, Francisco Rubio, Daniel P. Palomar, and Xavier Mestre, “Finite-Sample Linear Filter Optimization in Wireless Communications and Financial Systems,” IEEE Trans. on Signal Processing, vol. 61, no. 20, pp. 5014-5025, Oct. 2013.

• Italo Atzeni, Luis G. Ordóñez, Gesualdo Scutari, Daniel P. Palomar, and Javier R. Fonollosa, “Demand-Side Management via Distributed Energy Generation and Storage Optimization,” IEEE Trans. on Smart Grids, vol. 4, no. 2, pp. 866-876, June 2013.

• Jiaheng Wang, Mats Bengtsson, Björn Ottersten, and Daniel P. Palomar, “Robust MIMO Precoding for Several Classes of Channel Uncertainty,” IEEE Trans. on Signal Processing, vol. 61, no. 12, pp. 3056-3070, June 2013.

• Italo Atzeni, Luis G. Ordóñez, Gesualdo Scutari, Daniel P. Palomar, and Javier R. Fonollosa, “Noncooperative and Cooperative Optimization of Distributed Energy Generation and Storage in the Demand-Side of the Smart Grid,” IEEE Trans. on Signal Processing, vol. 61, no. 10, pp. 2454-2472, May 2013.

• Mengyi Zhang, Francisco Rubio, and Daniel P. Palomar, “Improved Calibration of High-Dimensional Precision Matrices,” IEEE Trans. on Signal Processing, vol. 61, no. 6, pp. 1509-1519, March 2013.

• Yongwei Huang, Daniel P. Palomar, and Shuzhong Zhang, “Lorentz-Positive Maps and Quadratic Matrix Inequalities with Applications to Robust MISO Transmit Beamforming,” IEEE Trans. on Signal Processing, vol. 61, no. 5, pp. 1121-1130, March 2013.

• Ronit Bustin, Miquel Payaró, Daniel P. Palomar, and Shlomo Shamai, “On MMSE Crossing Properties and Implications in Parallel Vector Gaussian Channels,” IEEE Trans. on Information Theory, vol. 59, no. 2, pp. 818-844, Feb. 2013.

2012

• Francisco Rubio, Xavier Mestre, and Daniel P. Palomar, “Performance Analysis and Optimal Selection of Large Minimum-Variance Portfolios under Estimation Risk,” IEEE Journal on Selected Topics in Signal Processing: Special Issue on Signal Processing Methods in Finance and Electronic Trading, vol. 6, no. 4, pp. 337-350, Aug. 2012.

• Luis G. Ordóñez, Daniel P. Palomar, and Javier R. Fonollosa, “Array Gain in the DMT Framework for MIMO Channels,” IEEE Trans. on Information Theory, vol. 58, no. 7, pp. 4577-4593, July 2012.

2011

• Javier Vía, Daniel P. Palomar, Luis Vielva, and Ignacio Santamaría, “Quaternion ICA from Second-Order Statistics,” IEEE Trans. on Signal Processing, vol. 59, no. 4, pp. 1586-1600, April 2011.
• Javier Vía, Daniel P. Palomar, and Luis Vielva, “Generalized Likelihood Ratios for Testing the Properness of Quaternion Gaussian Vectors,” IEEE Trans. on Signal Processing, vol. 59, no. 4, pp. 1356-1370, April 2011.
• Jiaheng Wang, Gesualdo Scutari, and Daniel P. Palomar, “Robust MIMO Cognitive Radio via Game Theory,” IEEE Trans. on Signal Processing, vol. 59, no. 3, pp. 1183-1201, March 2011.
• Antonio De Maio, Yongwei Huang, Daniel P. Palomar, Shuzhong Zhang, and Alfonso Farina, “Fractional QCQP with Applications in ML Steering Direction Estimation for Radar Detection,” IEEE Trans. on Signal Processing, vol. 59, no. 1, pp. 172-185, Jan. 2011.

2010

• Eduard Calvo, Daniel P. Palomar, Javier R. Fonollosa, and Josep Vidal, “On the Computation of the Capacity Region of the Discrete MAC,” IEEE Trans. on Communications, vol. 58, no. 12, pp. 3512-3525, Dec. 2010.
• Jiaheng Wang and Daniel P. Palomar, “Robust MMSE Precoding in MIMO Channels with Pre-Fixed Receivers,” IEEE Trans. on Signal Processing, vol. 58, no. 11, pp. 5802-5818, Nov. 2010.
• Yongwei Huang and Daniel P. Palomar, “A Dual Perspective on Separable Semidefinite Programming with Applications to Optimal Downlink Beamforming,” IEEE Trans. on Signal Processing, vol. 58, no. 8, pp. 4254-4271, Aug. 2010.
• Jong-Shi Pang, Gesualdo Scutari, Daniel P. Palomar, and Francisco Facchinei, “Design of Cognitive Radio Systems Under Temperature-Interference Constraints: A Variational Inequality Approach,” IEEE Trans. on Signal Processing, vol. 58, no. 6, pp. 3251-3271, June 2010.
• Gesualdo Scutari, Daniel P. Palomar, Francisco Facchinei, and Jong-Shi Pang, “Convex Optimization, Game Theory, and Variational Inequality Theory,” IEEE Signal Processing Magazine, vol. 27, no. 3, pp. 35-49, May 2010.
• Gesualdo Scutari and Daniel P. Palomar, “MIMO Cognitive Radio: A Game Theoretical Approach,” IEEE Trans. on Signal Processing, vol. 58, no. 2, pp. 761-780, Feb. 2010.
• Antonio De Maio, Silvio De Nicola, Yongwei Huang, Daniel P. Palomar, Shuzhong Zhang, and Alfonso Farina, “Code Design for Radar STAP via Optimization Theory,” IEEE Trans. on Signal Processing, vol. 58, no. 2, pp. 679-694, Feb. 2010.
• Yongwei Huang and Daniel P. Palomar, “Rank-Constrained Separable Semidefinite Programming With Applications to Optimal Beamforming,” IEEE Trans. on Signal Processing, vol. 58, no. 2, pp. 664-678, Feb. 2010.
  📈 Highly cited paper (ISI Web of Knowledge)

2009

• Svante Bergman, Daniel P. Palomar, and Björn Ottersten, “Joint Bit Allocation and Precoding for MIMO Systems with Decision Feedback Detection,” IEEE Trans. on Signal Processing, vol. 57, no. 11, pp. 4509-4521, Nov. 2009.
• Gesualdo Scutari, Daniel P. Palomar, Jong-Shi Pang, and Francisco Facchinei, “Flexible Design for Cognitive Wireless Systems: From Game Theory to Variational Inequality Theory,” IEEE Signal Processing Magazine, vol. 26, no. 5, pp. 107-123, Sept. 2009.
• Jiaheng Wang and Daniel P. Palomar, “Worst-Case Robust MIMO Transmission with Imperfect Channel Knowledge,” IEEE Trans. on Signal Processing, vol. 57, no. 8, pp. 3086-3100, Aug. 2009.
• Miquel Payaró and Daniel P. Palomar, “Hessian and Concavity of Mutual Information, Differential Entropy, and Entropy Power in Linear Vector Gaussian Channels,” IEEE Trans. on Information Theory, vol. 55, no. 8, pp. 3613-3628, Aug. 2009.
• Chee Wei Tan, Daniel P. Palomar, and Mung Chiang, “Energy-Robustness Tradeoff in Cellular Network Power Control,” IEEE/ACM Trans. on Networking, vol. 17, no. 3, pp. 912-925, June 2009.
• Luis G. Ordóñez, Daniel P. Palomar, Alba Pagès-Zamora, and Javier R. Fonollosa, “Minimum BER Linear MIMO Transceivers With Adaptive Number of Substreams,” IEEE Trans. on Signal Processing, vol. 57, no. 6, pp. 2336-2353, June 2009.
• Gesualdo Scutari, Daniel P. Palomar, and Sergio Barbarossa, “The MIMO Iterative Waterfilling Algorithm,” IEEE Trans. on Signal Processing, vol. 57, no. 5, pp. 1917-1935, May 2009.
• Luis G. Ordóñez, Daniel P. Palomar, and Javier R. Fonollosa, “Ordered Eigenvalues of a General Class of Hermitian Random Matrices With Application to the Performance Analysis of MIMO Systems,” IEEE Trans. on Signal Processing, vol. 57, no. 2, pp. 672-689, Feb. 2009.

2008

• Gesualdo Scutari, Daniel P. Palomar, and Sergio Barbarossa, “Cognitive MIMO Radio: Competitive Optimality Design Based on Subspace Projections,” IEEE Signal Processing Magazine, vol. 25, no. 6, pp. 46-59, Nov. 2008.

• Gesualdo Scutari, Daniel P. Palomar, and Sergio Barbarossa, “Competitive Design of Multiuser MIMO Systems based on Game Theory: A Unified View,” IEEE Journal on Selected Areas in Communications: Special Issue on Game Theory, vol. 25, no. 7, pp. 1089-1103, Sept. 2008.

• Xi Zhang, Daniel P. Palomar, and Björn Ottersten, “Statistically Robust Design of Linear MIMO Transceivers,” IEEE Trans. on Signal Processing, vol. 56, no. 8, pp. 3678-3689, Aug. 2008.

• Gesualdo Scutari, Daniel P. Palomar, and Sergio Barbarossa, “Asynchronous Iterative Water-Filling for Gaussian Frequency-Selective Interference Channels,” IEEE Trans. on Information Theory, vol. 54, no. 7, pp. 2868-2878, July 2008.

• Daniel P. Palomar and Sergio Verdú, “Lautum Information,” IEEE Trans. on Information Theory, vol. 54, no. 3, pp. 964-975, March 2008.

• Gesualdo Scutari, Daniel P. Palomar, and Sergio Barbarossa, “Optimal Linear Precoding Strategies for Wideband Noncooperative Systems Based on Game Theory – Part I: Nash Equilibria,” IEEE Trans. on Signal Processing, vol. 56, no. 3, pp. 1230-1249, March 2008.
  📈 Highly cited paper (ISI Web of Knowledge)

• Gesualdo Scutari, Daniel P. Palomar, and Sergio Barbarossa, “Optimal Linear Precoding Strategies for Wideband Noncooperative Systems Based on Game Theory – Part II: Algorithms,” IEEE Trans. on Signal Processing, vol. 56, no. 3, pp. 1250-1267, March 2008.
  📈 Highly cited paper (ISI Web of Knowledge)

2007

• Daniel P. Palomar and Mung Chiang, “Alternative Distributed Algorithms for Network Utility Maximization: Framework and Applications,” IEEE Trans. on Automatic Control, vol. 52, no. 12, pp. 2254-2269, Dec. 2007.

• Luis García-Ordoñez, Daniel P. Palomar, Alba Pagès-Zamora, and Javier R. Fonollosa, “High-SNR Analytical Performance of Spatial Multiplexing MIMO Systems with CSI,” IEEE Trans. on Signal Processing, vol. 55, no. 11, pp. 5447-5463, Nov. 2007.

• Mung Chiang, Chee Wei Tan, Daniel P. Palomar, Daniel O’Neill, and David Julian, “Power Control by Geometric Programming,” IEEE Trans. on Wireless Communications, vol. 6, no. 7, pp. 2640-2651, July 2007.
  📈 Highly cited paper (ISI Web of Knowledge)

• Daniel P. Palomar and Sergio Verdú, “Representation of Mutual Information via Input Estimates,” IEEE Trans. on Information Theory, vol. 53, no. 2, pp. 453-470, Feb. 2007.

2006

• Daniel P. Palomar and Mung Chiang, “A Tutorial on Decomposition Methods for Network Utility Maximization,” IEEE Journal on Selected Areas in Communications: Special Issue on Nonlinear Optimization of Communication Systems, vol. 24, no. 8, pp. 1439-1451, Aug. 2006.
  📈 Highly cited paper (ISI Web of Knowledge)

• Daniel P. Palomar and Sergio Verdú, “Gradient of Mutual Information in Linear Vector Gaussian Channels,” IEEE Trans. on Information Theory, vol. 52, no. 1, pp. 141-154, Jan. 2006.
  📈 Highly cited paper (ISI Web of Knowledge)

• A. Pascual-Iserte, Daniel P. Palomar, Ana I. Pérez-Neira, and Miguel A. Lagunas, “A Robust Maximin Approach for MIMO Communications with Partial Channel State Information Based on Convex Optimization,” IEEE Trans. on Signal Processing, vol. 54, no. 1, pp. 346-360, Jan. 2006.

2005

• Daniel P. Palomar, “Convex Primal Decomposition for Multicarrier Linear MIMO Transceivers,” IEEE Trans. on Signal Processing, vol. 53, no. 12, pp. 4661-4674, Dec. 2005.
• Daniel P. Palomar and Sergio Barbarossa, “Designing MIMO Communication Systems: Constellation Choice and Linear Transceiver Design,” IEEE Trans. on Signal Processing, vol. 53, no. 10, pp. 3804-3818, Oct. 2005.
• Daniel P. Palomar, Mats Bengtsson, and Björn Ottersten, “Minimum BER Linear Transceivers for MIMO Channels via Primal Decomposition,” IEEE Trans. on Signal Processing, vol. 53, no. 8, pp. 2866-2882, Aug. 2005.
• Daniel P. Palomar and Javier Rodriguez Fonollosa, “Practical Algorithms for a Family of Waterfilling Solutions,” IEEE Trans. on Signal Processing, vol. 53, no. 2, pp. 686-695, Feb. 2005.

2004

• Daniel P. Palomar, “Unified Framework for Linear MIMO Transceivers with Shaping Constraints,” IEEE Communications Letters, vol. 8, no. 12, pp. 697-699, Dec. 2004.

• Daniel P. Palomar, Miguel Angel Lagunas, and John M. Cioffi, “Optimum Linear Joint Transmit-Receive Processing for MIMO Channels with QoS Constraints,” IEEE Trans. on Signal Processing, vol. 52, no. 5, pp. 1179-1197, May 2004.

2003

• Daniel P. Palomar, John M. Cioffi, and Miguel Angel Lagunas, “Joint Tx-Rx Beamforming Design for Multicarrier MIMO Channels: A Unified Framework for Convex Optimization,” IEEE Trans. on Signal Processing, vol. 51, no. 9, pp. 2381-2401, Sept. 2003.
  🏆 2004 Young Author Best Paper Award by the IEEE Signal Processing Society
  📈 Highly cited paper (ISI Web of Knowledge)

• Daniel P. Palomar, John M. Cioffi, and Miguel Angel Lagunas, “Uniform Power Allocation in MIMO Channels: A Game-Theoretic Approach,” IEEE Trans. on Information Theory, vol. 49, no. 7, pp. 1707-1727, July 2003.

• Daniel P. Palomar and Miguel Angel Lagunas, “Joint Transmit-Receive Space-Time Equalization in Spatially Correlated MIMO channels: A Beamforming Approach,” IEEE Journal on Selected Areas in Communications: Special Issue on MIMO Systems and Applications, vol. 21, no. 5, pp. 730-743, June 2003.

Pre-2003

• Daniel P. Palomar and Miguel Angel Lagunas, “Temporal diversity on DS-CDMA communication systems for blind array signal processing,” EURASIP Signal Processing, vol. 81, no. 8, pp. 1625-1640, Aug. 2001.
• Daniel P. Palomar, Montse Nájar, and Miguel Angel Lagunas, “Self-reference Spatial Diversity Processing for Spread Spectrum Communications,” AEÜ International Journal of Electronics and Communications, vol. 54, no. 5, pp. 267-276, Nov. 2000.
• Daniel P. Palomar, Marc Price, and Mark Sandler, “Re-optimization of LPC filters for multi-pulse coded excitation,” IEE Electronics Letters, vol. 35, N. 13, pp. 1058-1059, ISSN 0013-5194, June 1999.

PhD Dissertation

• Daniel P. Palomar, A Unified Framework for Communications through MIMO Channels, Ph.D. dissertation, Technical University of Catalonia (UPC), Barcelona, Spain, May 2003.