Research Topics of Daniel P. Palomar

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Financial Engineering

Signal processing and financial engineering are seemingly different areas that share strong connections underneath. Both areas conceptually rely on the statistical analysis and modeling of systems as well as signals, either from the financial markets or from communication channels. In both cases, accurate characterization is essential to predict the behavior of practical algorithms and optimize their performance. The exploration of these connections will reveal ways to capitalize on existing mathematical tools and methodologies developed and widely applied in the context of signal processing applications. As a matter of fact, the techniques underlying optimal strategies for reliable communications over wireless links prove to be very useful in approaching open issues and recurring problems in quantitative finance.

Variational Inequality (VI) Methods for Multiuser Communication Systems

The Variation Inequality (VI) problem constitutes a very general class of problems in nonlinear analysis. The VI framework embraces many different types of problems such as systems of equations, optimization problems, equilibrium programming, complementary problems, saddle-point problems, Nash equilibrium problems, and generalized Nash equilibrium problems. It specially bears strong connections with game theory. There is a well-developed theory for the analysis of solutions of VIs, as well as a wide variety of efficient algorithms with convergence properties. Therefore, it constitutes an excellent tool for analyzing the previous problems and, in particular, game problems where the classical game-theoretic tools may fall short like in advanced cognitive radio systems.

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

  2. •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.

  3. •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.

  4. •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.

Quaternions

The use of complex numbers allows for a compact notation in many areas such as in baseband representation of communication systems. Quaternions constitute a further step: they are four-dimensional hypercomplex numbers. Quaternions have already found applications in image processing, wind modeling, processing of polarized waves, and design of space-time block codes.

  1. •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.

  2. •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.

MIMO Radar

Multiple-input multiple-output (MIMO) radar is an emerging concept that refers to a radar architecture that employs multiple, spatially distributed transmitters and receivers. This is reminiscent of MIMO systems in wireless communications where there are multiple transmit and receive antennas. This MIMO concept led to a revolution in wireless communications in the late 1990s and it is evident by now that one can exploit similar ideas in radar. This suggests interesting cross-fertilization of ideas between MIMO communications and MIMO radar. Among the many open problems in this emerging and revolutionary area, two important ones refer to i) the design and optimization of the transmitted waveforms, and ii) a convenient characterization of the performance that can in turn be used for the waveform design.

  1. •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.

  2. •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.

Rank-Constrained Semidefinite Programming

Semidefinite programming (SDP) is a class of convex optimization problems with a rich theory that can be efficiently solved in polynomial time. Many problems in wireless communications can be formulated as SDPs with additional rank constraints. Unfortunately, rank-constrained SDPs are nonconvex and are hard to solve in general (some of them are in fact NP-hard, but not all of them). One important example is beamforming design in the downlink of a cellular network with multi-antenna base stations transmitting to single-antenna users. Such a problem can be formulated as a rank-constrained SDP. We have developed a framework to quantify exactly what low-rank solutions can be achieved, as well as algorithms to obtain such solutions.

  1. •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.

  2. •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.

Cognitive Radio Systems via Game Theory

Radio regulatory bodies are recently recognizing that rigid spectrum assignment granting exclusive use to licensed services is highly inefficient. A more efficient way to utilize the scarce spectrum resources is with a dynamic spectrum access, depending on the real spectrum usage and traffic demands. The concept of cognitive radio has recently received great attention from the research community as a promising paradigm to achieve efficient use of the frequency resource by allowing the coexistence of licensed (primary) and unlicensed (secondary) users in the same bandwidth.

   We consider underlay/interweave multi-antenna networks, where primary users establish proper null and/or soft shaping constraints on the transmit covariance matrix of secondary users, so that the interference generated by secondary users is confined within the interference-temperature limits. The secondary users compete then for the resource allocation, which can be formally modeled with game theory to obtain a completely decentralized approach.

  1. •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.

  2. •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.

  3. •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. [draft]

  4. •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.

Robust Designs

The design of communication systems depends strongly on the degree of knowledge of the channel state information (CSI). The best spectral efficiency and/or performance is obviously achieved when perfect CSI is available at both sides of the link. However, in practical communication systems, imperfect CSI arises from a variety of sources such as channel estimation errors, quantization of the channel estimate in the feedback channel, and outdated channel estimates with respect to the current channel (for time-varying channels). When the CSI is imperfect, it is necessary to model such imperfections or uncertainties and develop robust designs that take them into account.

   There are two main philosophies for the design of systems robust to uncertainties: the worst-case approach, which considers that the uncertainty is within a given set around the nominal estimated value, and the Bayesian approach, which models the uncertainty statistically. The worst-case design guarantees a certain system performance for any possible channel sufficiently close to the estimated one, whereas the Bayesian design guarantees a certain system performance averaged over the channel realizations. We consider both perspectives in the design of robust MIMO communication systems.

  1. •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.

  2. •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.

  3. •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.

  4. •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.

  5. •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.

  6. •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.

Game Theory for Competitive Communications

Many communication systems of interest contain multiple uncoordinated users that share a common medium (e.g., wireless ad-hoc networks). These systems can be mathematically modeled as the so-called interference channel, for which the capacity region is still unknown. In these scenarios, centralized solutions are to be avoided and distributed algorithms play a central role. In fact, in many cases, the right model is to consider selfish users that compete with each other for the resources. Game theory is the right framework to study such competitive networks of users, leading to the concept of Nash equilibrium (NE) as solution of the game. This results in fully distributed algorithms with no signaling required among the users. Within the context of game theory, there are three main aspects to be studied: i) existence of NE, ii) uniqueness of NE, and iii) development of practical distributed algorithms with provable convergence to the NE.

   This problem has been studied since 2001 by different researchers in the frequency-selective case. Among other things, we provide the state-of-the-art conditions for convergence of a class of iterative algorithms that contain as special cases the popular sequential iterative waterfilling algorithms as well as the novel asynchronous iterative waterfilling algorithm (more realistic in practice). We then extend the analysis to the MIMO case which provides a unified view of the problem. As a side result, we provide a novel interpretation of the (MIMO) waterfilling operator as a projection onto a proper convex set (this result is in fact fundamental to prove contraction mapping and convergence of algorithms).

  1. •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.

  2. •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.

  3. •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.

  4. •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.

  5. •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.

Information Theory and Estimation Theory

Information theory and estimation theory have generally been regarded as two separate theories with little overlap. Recently, however, it has been recognized that the relations between the two theories are fundamental (e.g., relating the mutual information with the minimum mean-square error) and can indeed be very useful to transfer results from one area to the other. In addition to the intrinsic theoretical interest of such relations, they have already found several applications such as the mercury/waterfilling optimal power allocation over a set of parallel Gaussian channels, a simple proof for the entropy power inequality, a simple proof of the monotonicity of the non-Gaussianness of independent random variables, and the study of extrinsic information of good codes.

   We have further explored these connections in the vector Gaussian and arbitrary (non-Gaussian) settings. One interesting application of such a characterization is the efficient computation of the mutual information achieved by a given code over a channel via the symbolwise a posteriori probabilities (which previously could not be computed). We have also considered an alternative definition of information termed lautum information (different from mutual information).

  1. •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.

  2. •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.

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

  4. •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.

  5. •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.

Random Matrix Theory for Communication Systems

The performance of multiple-input multiple-output (MIMO) communication systems is related to the eigenstructure of the channel matrix H (channel eigenmodes) or, more exactly, to the non-zero eigenvalues of HH†. Therefore, the probabilistic characterization of these eigenvalues is necessary in order to derive analytical expressions for the average and outage performance measures of the system. In MIMO wireless communications, the channel matrix H is commonly modeled with Gaussian distributed entries. This results in HH† being a Wishart random matrix. The Wishart distribution and some closely related distributions have been widely studied during the sixties and seventies in the mathematical literature, due to its importance in various areas of research such as the analysis of time series or nuclear physics. More recently, the statistical properties of the eigenvalues of Wishart matrices have been investigated and effectively applied to analyze the information theoretical limits of MIMO channels as well as the performance of practical MIMO systems.

   We consider a class of Hermitian random matrices that contains as particular cases the classical Wishart, the correlated central Wishart, the correlated central Pseudo-Wishart, and the noncentral Wishart. We first obtain expressions for the distribution of the ordered eigenvalues; in particular, for i) the joint cdf, ii) the marginal cdf’s, and iii) the marginal pdf’s. Then, for simpler tractability, we develop first-order Taylor expansions that translate into convenient SNR gain and diversity gain characterizations of the performance in communication systems.

  1. •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.

  2. •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.

  3. •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.

Cross-Layer Network Optimization

During the last decade, it has been widely recognized that an independent optimization of the different OSI layers in a communication system is a limiting design factor. Instead, a cross-layer design is necessary. In this sense, network utility maximization (NUM) problem formulations provide an important approach to conduct network resource allocation and to view layering as optimization decomposition.

   In the previous existing literature, distributed implementations were typically achieved by means of the so-called dual decomposition technique. However, the span of decomposition possibilities includes many other elements that had not been fully exploited, such as the use of the primal decomposition technique, the versatile introduction of auxiliary variables, and the potential of multilevel decompositions. We have developed a systematic framework to exploit alternative decomposition structures as a way to obtain different distributed algorithms, each with a different tradeoff among convergence speed, message passing amount and asymmetry, and distributed computation architecture. Some illustrative applications include resource-constrained and direct-control rate allocation, and rate allocation among QoS classes with multipath routing.

  1. •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.

  2. •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.

  3. •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.

  4. •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.

  5. •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. [draft]

MIMO Communication Systems via Convex Optimization Theory and Majorization Theory

Multiple-input multiple-output (MIMO) channels provide an abstract and unified representation of different physical communication systems, ranging from multi-antenna wireless channels to wireless digital subscriber line systems. They have the key property that several data streams can be simultaneously established. In general, the design of communication systems for MIMO channels is quite involved. The first diffculty lies on how to measure the global performance of such systems given the tradeoff on the performance among the different data streams. Once the problem formulation is defined, the resulting mathematical problem is typically too complicated to be optimally solved as it is a matrix-valued nonconvex optimization problem.

   This design problem has been studied for the past three decades (the first papers dating back to the 1970s) motivated initially by cable systems and more recently by wireless multi-antenna systems. The approach was to choose a specific global measure of performance and then to design the system accordingly, either optimally or suboptimally, depending on the difficulty of the problem. We develop a novel unified mathematical framework for the design of point-to-point MIMO transceivers with channel state information at both sides of the link according to an arbitrary cost function as a measure of the system performance. Majorization theory is the underlying mathematical theory on which the framework hinges. It allows the transformation of the originally complicated matrix-valued nonconvex problem into a simple scalar problem. In particular, the additive majorization relation plays a key role in the design of linear MIMO transceivers (i.e., a linear precoder at the transmitter and a linear equalizer at the receiver), whereas the multiplicative majorization relation is the basis for nonlinear decision- feedback MIMO transceivers (i.e., a linear precoder at the transmitter and a decision-feedback equalizer at the receiver).

  1. •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, pp. 331-551, 2006.  [book version] [journal version] [typos]

  2. •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, Eds. Thomas Chen, Dinesh Rajan, and Erchin Serpedin, Cambridge University Press, 2011.

  3. •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.

  4. •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. [draft]

  5. •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. [draft]

  6. •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.

  7. •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.

  8. •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.

  9. •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.

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

  11. •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.

  12. •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.

  13. Made on a Mac
    2004 Young Author Best Paper Award by the IEEE Signal Processing Society

  14.      and

  15. Highly cited paper (ISI Web of Knowledge)

  16. •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.

Blind Beamforming in CDMA Systems

Beamforming in wireless communication systems with multiple receive antennas has been studied for the last three decades. Traditionally, the design of the beamformer is based on either the knowledge of the spatial signature of the signal of interest or the availability of a training sequence. It is, however, possible to use blind techniques to design the beamformer without any spatial reference or training sequence. This requires knowledge of some structural property of the desired signal (e.g., the constant modulus blind techniques).

   We consider Spread Spectrum signals, which contain a rich time/frequency structure, for blind beamforming. One example is CDMA systems, where the temporal structure is given by the codes. It is possible to have coexisting users using different codes and still implement blind beamforming for each of the users.

  1. •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.

  2. •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.