


default search action
Daniel Pérez Palomar
Daniel P. Palomar
Person information
- affiliation: Hong Kong University of Science and Technology
Refine list

refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2025
- [j118]Jasin Machkour
, Michael Muma
, Daniel P. Palomar
:
The terminating-random experiments selector: Fast high-dimensional variable selection with false discovery rate control. Signal Process. 231: 109894 (2025) - [i44]Amirhossein Javaheri, Jiaxi Ying, Daniel P. Palomar, Farokh Marvasti:
Time-Varying Graph Learning for Data with Heavy-Tailed Distribution. CoRR abs/2501.00606 (2025) - 2024
- [j117]Amirhossein Javaheri
, Arash Amini
, Farokh Marvasti
, Daniel P. Palomar
:
Learning Spatiotemporal Graphical Models From Incomplete Observations. IEEE Trans. Signal Process. 72: 1361-1374 (2024) - [j116]Runhao Shi
, Daniel P. Palomar
:
SAOFTRL: A Novel Adaptive Algorithmic Framework for Enhancing Online Portfolio Selection. IEEE Trans. Signal Process. 72: 5291-5305 (2024) - [c118]Amirhossein Javaheri, Daniel P. Palomar:
Learning Time-Varying Graphs for Heavy-Tailed Data Clustering. EUSIPCO 2024: 2472-2476 - [c117]Jasin Machkour, Michael Muma, Daniel P. Palomar:
FDR-Controlled Sparse Index Tracking with Autoregressive Stock Dependency Models. EUSIPCO 2024: 2662-2666 - [c116]Jasin Machkour, Arnaud Breloy, Michael Muma, Daniel P. Palomar, Frédéric Pascal:
Sparse PCA with False Discovery Rate Controlled Variable Selection. ICASSP 2024: 9716-9720 - [c115]Amirhossein Javaheri, Arash Amini, Farokh Marvasti, Daniel P. Palomar:
Joint Signal Recovery and Graph Learning from Incomplete Time-Series. ICASSP 2024: 13511-13515 - [c114]Runhao Shi, Jiaxi Ying, Daniel P. Palomar:
Adaptive Passive-Aggressive Framework for Online Regression with Side Information. NeurIPS 2024 - [i43]Jasin Machkour, Arnaud Breloy, Michael Muma, Daniel P. Palomar, Frédéric Pascal:
Sparse PCA with False Discovery Rate Controlled Variable Selection. CoRR abs/2401.08375 (2024) - [i42]Jasin Machkour, Daniel P. Palomar, Michael Muma:
FDR-Controlled Portfolio Optimization for Sparse Financial Index Tracking. CoRR abs/2401.15139 (2024) - [i41]Andrei Buciulea, Jiaxi Ying, Antonio G. Marques, Daniel P. Palomar:
Polynomial Graphical Lasso: Learning Edges from Gaussian Graph-Stationary Signals. CoRR abs/2404.02621 (2024) - 2023
- [j115]Esa Ollila
, Daniel P. Palomar
, Frédéric Pascal
:
Affine Equivariant Tyler's M-Estimator Applied to Tail Parameter Learning of Elliptical Distributions. IEEE Signal Process. Lett. 30: 1017-1021 (2023) - [j114]Shengjie Xiu
, Xiwen Wang
, Daniel P. Palomar
:
A Fast Successive QP Algorithm for General Mean-Variance Portfolio Optimization. IEEE Trans. Signal Process. 71: 2713-2727 (2023) - [j113]Xiwen Wang
, Rui Zhou
, Jiaxi Ying
, Daniel P. Palomar
:
Efficient and Scalable Parametric High-Order Portfolios Design via the Skew-$t$ Distribution. IEEE Trans. Signal Process. 71: 3726-3740 (2023) - [c113]Jasin Machkour, Michael Muma, Daniel P. Palomar:
The Informed Elastic Net for Fast Grouped Variable Selection and FDR Control in Genomics Research. CAMSAP 2023: 466-470 - [c112]Amirhossein Javaheri, José Vinícius de Miranda Cardoso, Daniel P. Palomar:
Graph Learning for Balanced Clustering of Heavy-Tailed Data. CAMSAP 2023: 481-485 - [c111]Shengjie Xiu, Daniel P. Palomar:
Intraday Volatility-Volume Joint Modeling and Forecasting: A State-Space Approach. EUSIPCO 2023: 1395-1399 - [c110]José Vinícius de Miranda Cardoso, Jiaxi Ying, Sandeep Kumar, Daniel P. Palomar:
Estimating Normalized Graph Laplacians in Financial Markets. ICASSP 2023: 1-5 - [c109]Jiaxi Ying, José Vinícius de Miranda Cardoso, Daniel P. Palomar:
Adaptive Estimation of Graphical Models under Total Positivity. ICML 2023: 40054-40074 - [c108]Jianfeng Cai, José Vinícius de Miranda Cardoso, Daniel P. Palomar, Jiaxi Ying:
Fast Projected Newton-like Method for Precision Matrix Estimation under Total Positivity. NeurIPS 2023 - [c107]Xiwen Wang, Jiaxi Ying, Daniel P. Palomar:
Learning Large-Scale MTP2 Gaussian Graphical Models via Bridge-Block Decomposition. NeurIPS 2023 - [c106]Chenyu Gao, Ziping Zhao, Daniel P. Palomar:
A Novel Algorithm for GARCH Model Estimation. SSP 2023: 210-214 - [c105]Jasin Machkour, Michael Muma, Daniel P. Palomar:
False Discovery Rate Control for Fast Screening of Large-Scale Genomics Biobanks. SSP 2023: 666-670 - [i40]Xiwen Wang, Jiaxi Ying, Daniel P. Palomar:
Learning Large-Scale MTP2 Gaussian Graphical Models via Bridge-Block Decomposition. CoRR abs/2309.13405 (2023) - [i39]Zepeng Zhang, Ziping Zhao, Kaiming Shen, Daniel P. Palomar, Wei Yu:
Discerning and Enhancing the Weighted Sum-Rate Maximization Algorithms in Communications. CoRR abs/2311.04546 (2023) - [i38]Amirhossein Javaheri, Arash Amini, Farokh Marvasti, Daniel P. Palomar:
Joint Signal Recovery and Graph Learning from Incomplete Time-Series. CoRR abs/2312.16940 (2023) - 2022
- [j112]Rui Zhou
, Jiaxi Ying
, Daniel P. Palomar
:
Covariance Matrix Estimation Under Low-Rank Factor Model With Nonnegative Correlations. IEEE Trans. Signal Process. 70: 4020-4030 (2022) - [c104]Xiwen Wang
, Jiaxi Ying, José Vinícius de Miranda Cardoso, Daniel P. Palomar:
Efficient Algorithms for General Isotone Optimization. AAAI 2022: 8575-8583 - [c103]Jasin Machkour, Michael Muma, Daniel P. Palomar:
False Discovery Rate Control for Grouped Variable Selection in High-Dimensional Linear Models Using the T-Knock Filter. EUSIPCO 2022: 892-896 - [c102]José Vinícius de Miranda Cardoso, Jiaxi Ying, Daniel P. Palomar:
Learning Bipartite Graphs: Heavy Tails and Multiple Components. NeurIPS 2022 - [i37]Jiaxi Ying, José Vinícius de Miranda Cardoso, Daniel P. Palomar:
Adaptive Estimation of MTP2 Graphical Models. CoRR abs/2210.15471 (2022) - 2021
- [j111]Esa Ollila
, Daniel P. Palomar
, Frédéric Pascal:
Shrinking the Eigenvalues of M-Estimators of Covariance Matrix. IEEE Trans. Signal Process. 69: 256-269 (2021) - [j110]Rui Zhou
, Daniel P. Palomar
:
Solving High-Order Portfolios via Successive Convex Approximation Algorithms. IEEE Trans. Signal Process. 69: 892-904 (2021) - [j109]Arnaud Breloy
, Sandeep Kumar
, Ying Sun
, Daniel P. Palomar
:
Majorization-Minimization on the Stiefel Manifold With Application to Robust Sparse PCA. IEEE Trans. Signal Process. 69: 1507-1520 (2021) - [c101]Jiaxi Ying
, José Vinícius de Miranda Cardoso, Daniel P. Palomar
:
A Fast Algorithm for Graph Learning under Attractive Gaussian Markov Random Fields. ACSCC 2021: 1520-1524 - [c100]Jiaxi Ying, José Vinícius de Miranda Cardoso, Daniel P. Palomar:
Minimax Estimation of Laplacian Constrained Precision Matrices. AISTATS 2021: 3736-3744 - [c99]Frédéric Pascal, Esa Ollila
, Daniel P. Palomar
:
Improved estimation of the degree of freedom parameter of multivariate $t$-distribution. EUSIPCO 2021: 860-864 - [c98]Rui Zhou, Junyan Liu, Sandeep Kumar, Daniel P. Palomar
:
Parameter Estimation for Student's t VAR Model with Missing Data. ICASSP 2021: 5145-5149 - [c97]José Vinícius de Miranda Cardoso, Jiaxi Ying, Daniel P. Palomar:
Graphical Models in Heavy-Tailed Markets. NeurIPS 2021: 19989-20001 - [i36]Jiaxi Ying, José Vinícius de Miranda Cardoso, Jian-Feng Cai, Daniel P. Palomar:
Fast Projected Newton-like Method for Precision Matrix Estimation with Nonnegative Partial Correlations. CoRR abs/2112.01939 (2021) - 2020
- [j108]Sandeep Kumar, Jiaxi Ying, José Vinícius de Miranda Cardoso, Daniel P. Palomar:
A Unified Framework for Structured Graph Learning via Spectral Constraints. J. Mach. Learn. Res. 21: 22:1-22:60 (2020) - [j107]Linlong Wu
, Yiyong Feng, Daniel P. Palomar
:
General sparse risk parity portfolio design via successive convex optimization. Signal Process. 170: 107433 (2020) - [j106]Rui Zhou
, Daniel P. Palomar
:
Understanding the Quintile Portfolio. IEEE Trans. Signal Process. 68: 4030-4040 (2020) - [j105]Rui Zhou
, Junyan Liu
, Sandeep Kumar
, Daniel P. Palomar
:
Student's $t$ VAR Modeling With Missing Data Via Stochastic EM and Gibbs Sampling. IEEE Trans. Signal Process. 68: 6198-6211 (2020) - [c96]José Vinícius de Miranda Cardoso, Daniel P. Palomar
:
Learning Undirected Graphs in Financial Markets. ACSSC 2020: 741-745 - [c95]Esa Ollila
, Daniel P. Palomar
, Frédéric Pascal:
M-Estimators of Scatter with Eigenvalue Shrinkage. ICASSP 2020: 5305-5309 - [c94]Rui Zhou, Daniel P. Palomar
:
A Theoretical Basis for Practitioners Heuristic 1/N and Long-Only Quintile Portfolio. ICASSP 2020: 8434-8438 - [c93]Jiaxi Ying, José Vinícius de Miranda Cardoso, Daniel P. Palomar:
Nonconvex Sparse Graph Learning under Laplacian Constrained Graphical Model. NeurIPS 2020 - [i35]Jiaxi Ying, José Vinícius de Miranda Cardoso, Daniel P. Palomar:
Does the 𝓁1-norm Learn a Sparse Graph under Laplacian Constrained Graphical Models? CoRR abs/2006.14925 (2020) - [i34]José Vinícius de Miranda Cardoso, Jiaxi Ying, Daniel Pérez Palomar:
Algorithms for Learning Graphs in Financial Markets. CoRR abs/2012.15410 (2020)
2010 – 2019
- 2019
- [j104]Junyan Liu
, Daniel P. Palomar
:
Regularized robust estimation of mean and covariance matrix for incomplete data. Signal Process. 165: 278-291 (2019) - [j103]Kaiming Shen
, Wei Yu
, Licheng Zhao, Daniel P. Palomar
:
Optimization of MIMO Device-to-Device Networks via Matrix Fractional Programming: A Minorization-Maximization Approach. IEEE/ACM Trans. Netw. 27(5): 2164-2177 (2019) - [j102]Ziping Zhao
, Rui Zhou
, Daniel P. Palomar
:
Optimal Mean-Reverting Portfolio With Leverage Constraint for Statistical Arbitrage in Finance. IEEE Trans. Signal Process. 67(7): 1681-1695 (2019) - [j101]Junyan Liu
, Sandeep Kumar
, Daniel P. Palomar
:
Parameter Estimation of Heavy-Tailed AR Model With Missing Data Via Stochastic EM. IEEE Trans. Signal Process. 67(8): 2159-2172 (2019) - [j100]Licheng Zhao
, Yiwei Wang
, Sandeep Kumar
, Daniel P. Palomar
:
Optimization Algorithms for Graph Laplacian Estimation via ADMM and MM. IEEE Trans. Signal Process. 67(16): 4231-4244 (2019) - [j99]Linlong Wu
, Daniel P. Palomar
:
Sequence Design for Spectral Shaping via Minimization of Regularized Spectral Level Ratio. IEEE Trans. Signal Process. 67(18): 4683-4695 (2019) - [c92]Sandeep Kumar, Jiaxi Ying
, José Vinícius de Miranda Cardoso
, Daniel P. Palomar
:
Bipartite Structured Gaussian Graphical Modeling via Adjacency Spectral Priors. ACSSC 2019: 322-326 - [c91]Rui Zhou, Daniel P. Palomar
:
Accelerating the Multivariate SKEW T Parameter Estimation. CAMSAP 2019: 251-255 - [c90]Rui Zhou, Junyan Liu, Sandeep Kumar, Daniel P. Palomar
:
Robust Factor Analysis Parameter Estimation. EUROCAST (2) 2019: 3-11 - [c89]Junyan Liu, Sandeep Kumar, Daniel P. Palomar
:
Parameter Estimation of Heavy-Tailed AR(p) Model from Incomplete Data. EUSIPCO 2019: 1-5 - [c88]Ziping Zhao, Daniel P. Palomar
:
Large-Scale Regularized Portfolio Selection Via Convex Optimization. GlobalSIP 2019: 1-5 - [c87]Rui Zhou, Ziping Zhao, Daniel P. Palomar
:
Unified Framework for Minimax MIMO Transmit Beampattern Matching under Waveform Constraints. ICASSP 2019: 4150-4154 - [c86]Sandeep Kumar, Jiaxi Ying, José Vinícius de Miranda Cardoso, Daniel P. Palomar:
Structured Graph Learning Via Laplacian Spectral Constraints. NeurIPS 2019: 11647-11658 - [i33]Sandeep Kumar, Jiaxi Ying, José Vinícius de Miranda Cardoso, Daniel P. Palomar:
A Unified Framework for Structured Graph Learning via Spectral Constraints. CoRR abs/1904.09792 (2019) - [i32]Sandeep Kumar, Ketan Rajawat, Daniel P. Palomar:
Distributed Inexact Successive Convex Approximation ADMM: Analysis-Part I. CoRR abs/1907.08969 (2019) - [i31]Sandeep Kumar, Jiaxi Ying, José Vinícius de Miranda Cardoso, Daniel P. Palomar:
Structured Graph Learning Via Laplacian Spectral Constraints. CoRR abs/1909.11594 (2019) - 2018
- [j98]Konstantinos Benidis, Yiyong Feng, Daniel P. Palomar:
Optimization Methods for Financial Index Tracking: From Theory to Practice. Found. Trends Optim. 3(3): 171-279 (2018) - [j97]Konstantinos Benidis
, Yiyong Feng, Daniel P. Palomar
:
Sparse Portfolios for High-Dimensional Financial Index Tracking. IEEE Trans. Signal Process. 66(1): 155-170 (2018) - [j96]Linlong Wu
, Prabhu Babu, Daniel P. Palomar
:
Transmit Waveform/Receive Filter Design for MIMO Radar With Multiple Waveform Constraints. IEEE Trans. Signal Process. 66(6): 1526-1540 (2018) - [j95]Tianyu Qiu
, Xiao Fu
, Nicholas D. Sidiropoulos
, Daniel P. Palomar
:
MISO Channel Estimation and Tracking from Received Signal Strength Feedback. IEEE Trans. Signal Process. 66(7): 1691-1704 (2018) - [j94]Ziping Zhao
, Daniel P. Palomar
:
Mean-Reverting Portfolio With Budget Constraint. IEEE Trans. Signal Process. 66(9): 2342-2357 (2018) - [j93]Licheng Zhao
, Daniel P. Palomar
:
A Markowitz Portfolio Approach to Options Trading. IEEE Trans. Signal Process. 66(16): 4223-4238 (2018) - [c85]Ziping Zhao, Songtao Lu, Mingyi Hong, Daniel P. Palomar
:
Distributed optimization for Generalized Phase Retrieval Over Networks. ACSSC 2018: 48-52 - [c84]Ziping Zhao
, Daniel P. Palomar
:
MIMO Transmit Beampattern Matching Under Waveform Constraints. ICASSP 2018: 3281-3285 - [c83]Junyan Liu, Sandeep Kumar, Daniel P. Palomar
:
Parameter Estimation of Heavy-Tailed Random Walk Model from Incomplete Data. ICASSP 2018: 4439-4443 - [c82]Ziping Zhao
, Rui Zhou, Zhongju Wang
, Daniel P. Palomar
:
Optimal Portfolio Design for Statistical Arbitrage in Finance. SSP 2018: 801-805 - [c81]Ziping Zhao
, Daniel P. Palomar
:
Sparse Reduced Rank Regression with Nonconvex Regularization. SSP 2018: 811-815 - [i30]Ziping Zhao, Daniel P. Palomar:
MIMO Transmit Beampattern Matching Under Waveform Constraints. CoRR abs/1802.06957 (2018) - [i29]Ziping Zhao, Daniel P. Palomar:
Sparse Reduced Rank Regression With Nonconvex Regularization. CoRR abs/1803.07247 (2018) - [i28]Kaiming Shen, Wei Yu, Licheng Zhao, Daniel P. Palomar:
Coordinated Scheduling and Spectrum Sharing via Matrix Fractional Programming. CoRR abs/1808.05678 (2018) - 2017
- [j92]Abdelhak M. Zoubir, Jorge Plata-Chaves, Daniel Pérez Palomar
, Anna Scaglione, Alejandro Ribeiro:
Introduction to the Issue on Cooperative Signal Processing for Heterogeneous and Multi-Task Wireless Sensor Networks. IEEE J. Sel. Top. Signal Process. 11(3): 447-449 (2017) - [j91]Javier Rubio
, Antonio Pascual-Iserte
, Daniel P. Palomar
, Andrea Goldsmith:
Joint Optimization of Power and Data Transfer in Multiuser MIMO Systems. IEEE Trans. Signal Process. 65(1): 212-227 (2017) - [j90]Licheng Zhao, Junxiao Song, Prabhu Babu, Daniel P. Palomar
:
A Unified Framework for Low Autocorrelation Sequence Design via Majorization-Minimization. IEEE Trans. Signal Process. 65(2): 438-453 (2017) - [j89]Linlong Wu
, Prabhu Babu, Daniel P. Palomar
:
Cognitive Radar-Based Sequence Design via SINR Maximization. IEEE Trans. Signal Process. 65(3): 779-793 (2017) - [j88]Ying Sun
, Prabhu Babu, Daniel P. Palomar
:
Majorization-Minimization Algorithms in Signal Processing, Communications, and Machine Learning. IEEE Trans. Signal Process. 65(3): 794-816 (2017) - [j87]Licheng Zhao, Daniel P. Palomar
:
Maximin Joint Optimization of Transmitting Code and Receiving Filter in Radar and Communications. IEEE Trans. Signal Process. 65(4): 850-863 (2017) - [j86]Zhongju Wang
, Prabhu Babu, Daniel P. Palomar
:
Effective Low-Complexity Optimization Methods for Joint Phase Noise and Channel Estimation in OFDM. IEEE Trans. Signal Process. 65(12): 3247-3260 (2017) - [c80]Junyan Liu, Daniel P. Palomar
:
Robust estimation of mean and covariance matrix for incomplete data in financial applications. GlobalSIP 2017: 908-912 - [c79]Ziping Zhao
, Daniel P. Palomar
:
Robust maximum likelihood estimation of sparse vector error correction model. GlobalSIP 2017: 913-917 - [c78]Zhongju Wang
, Prabhu Babu, Daniel P. Palomar
:
A low-complexity algorithm for OFDM phase noise estimation. SPAWC 2017: 1-5 - [c77]Linlong Wu
, Prabhu Babu, Daniel P. Palomar
:
A fast algorithm for joint design of transmit waveforms and receive filters. SPAWC 2017: 1-5 - [i27]Ziping Zhao, Daniel P. Palomar:
Robust Maximum Likelihood Estimation of Sparse Vector Error Correction Model. CoRR abs/1710.05513 (2017) - 2016
- [j85]Yiyong Feng, Daniel P. Palomar
:
A Signal Processing Perspective of Financial Engineering. Found. Trends Signal Process. 9(1-2) (2016) - [j84]Ali N. Akansu, Dmitry Malioutov, Daniel P. Palomar
, Emmanuelle Jay, Danilo P. Mandic:
Introduction to the Issue on Financial Signal Processing and Machine Learning for Electronic Trading. IEEE J. Sel. Top. Signal Process. 10(6): 979-981 (2016) - [j83]Yang Yang, Marius Pesavento
, Mengyi Zhang, Daniel P. Palomar
:
An Online Parallel Algorithm for Recursive Estimation of Sparse Signals. IEEE Trans. Signal Inf. Process. over Networks 2(3): 290-305 (2016) - [j82]Ying Sun, Arnaud Breloy, Prabhu Babu, Daniel P. Palomar
, Frédéric Pascal, Guillaume Ginolhac:
Low-Complexity Algorithms for Low Rank Clutter Parameters Estimation in Radar Systems. IEEE Trans. Signal Process. 64(8): 1986-1998 (2016) - [j81]Junxiao Song, Prabhu Babu, Daniel P. Palomar
:
Sequence Design to Minimize the Weighted Integrated and Peak Sidelobe Levels. IEEE Trans. Signal Process. 64(8): 2051-2064 (2016) - [j80]Junxiao Song, Prabhu Babu, Daniel P. Palomar
:
Sequence Set Design With Good Correlation Properties Via Majorization-Minimization. IEEE Trans. Signal Process. 64(11): 2866-2879 (2016) - [j79]Yang Yang, Gesualdo Scutari, Daniel P. Palomar
, Marius Pesavento
:
A Parallel Decomposition Method for Nonconvex Stochastic Multi-Agent Optimization Problems. IEEE Trans. Signal Process. 64(11): 2949-2964 (2016) - [j78]Ying Sun
, Prabhu Babu, Daniel Pérez Palomar
:
Robust Estimation of Structured Covariance Matrix for Heavy-Tailed Elliptical Distributions. IEEE Trans. Signal Process. 64(14): 3576-3590 (2016) - [j77]Licheng Zhao
, Prabhu Babu, Daniel P. Palomar
:
Efficient Algorithms on Robust Low-Rank Matrix Completion Against Outliers. IEEE Trans. Signal Process. 64(18): 4767-4780 (2016) - [j76]Tianyu Qiu
, Prabhu Babu, Daniel Pérez Palomar
:
PRIME: Phase Retrieval via Majorization-Minimization. IEEE Trans. Signal Process. 64(19): 5174-5186 (2016) - [j75]Zhongju Wang
, Prabhu Babu, Daniel P. Palomar
:
Design of PAR-Constrained Sequences for MIMO Channel Estimation via Majorization-Minimization. IEEE Trans. Signal Process. 64(23): 6132-6144 (2016) - [j74]Konstantinos Benidis, Ying Sun
, Prabhu Babu, Daniel P. Palomar
:
Orthogonal Sparse PCA and Covariance Estimation via Procrustes Reformulation. IEEE Trans. Signal Process. 64(23): 6211-6226 (2016) - [j73]Maria Gregori, Miquel Payaró, Daniel Pérez Palomar
:
Sum-Rate Maximization for Energy Harvesting Nodes With a Generalized Power Consumption Model. IEEE Trans. Wirel. Commun. 15(8): 5341-5354 (2016) - [c76]Ying Sun, Gesualdo Scutari, Daniel Pérez Palomar
:
Distributed nonconvex multiagent optimization over time-varying networks. ACSSC 2016: 788-794 - [c75]Arnaud Breloy, Ying Sun, Prabhu Babu, Guillaume Ginolhac, Daniel Pérez Palomar
:
Robust rank constrained kronecker covariance matrix estimation. ACSSC 2016: 810-814 - [c74]Ziping Zhao
, Daniel P. Palomar
:
Mean-reverting portfolio design via majorization-minimization method. ACSSC 2016: 1530-1534 - [c73]Arnaud Breloy, Ying Sun, Prabhu Babu, Daniel Pérez Palomar
:
Block majorization-minimization algorithms for low-rank clutter subspace estimation. EUSIPCO 2016: 2186-2190 - [c72]Junxiao Song, Prabhu Babu, Daniel Pérez Palomar
:
Sequence design to minimize the peak sidelobe level. ICASSP 2016: 3896-3900 - [c71]Zhongju Wang
, Prabhu Babu, Daniel P. Palomar
:
Optimal design of constant-modulus channel training sequences. ICASSP 2016: 3901-3905 - [c70]Konstantinos Benidis, Ying Sun, Prabhu Babu, Daniel Pérez Palomar
:
Orthogonal sparse eigenvectors: A procrustes problem. ICASSP 2016: 4683-4686 - [c69]Yiyong Feng, Daniel Pérez Palomar
:
Portfolio optimization with asset selection and risk parity control. ICASSP 2016: 6585-6589 - [c68]Javier Rubio, Antonio Pascual-Iserte
, Daniel P. Palomar
, Andrea Goldsmith:
SWIPT techniques for multiuser MIMO broadcast systems. PIMRC 2016: 1-6 - [c67]Arnaud Breloy, Ying Sun, Prabhu Babu, Guillaume Ginolhac, Daniel Pérez Palomar
, Frédéric Pascal:
A robust signal subspace estimator. SSP 2016: 1-4 - [i26]Konstantinos Benidis, Ying Sun, Prabhu Babu, Daniel Pérez Palomar:
Orthogonal Sparse PCA and Covariance Estimation via Procrustes Reformulation. CoRR abs/1602.03992 (2016) - [i25]Zhongju Wang
, Prabhu Babu, Daniel P. Palomar:
Design of PAR-Constrained Sequences for MIMO Channel Estimation via Majorization-Minimization. CoRR abs/1602.08877 (2016) - [i24]Javier Rubio, Antonio Pascual-Iserte, Daniel Pérez Palomar, Andrea Goldsmith:
Joint Optimization of Power and Data Transfer in Multiuser MIMO Systems. CoRR abs/1604.00434 (2016) - [i23]