Nicholas D. Sidiropoulos

Blind PARAFAC receivers for DS-CDMA systems

This paper links the direct-sequence code-division multiple access (DS-CDMA) multiuser separation-equalization-detection problem to the parallel factor (PARAFAC) model, which is an analysis tool rooted in psychometrics and chemometrics. Exploiting this link, it derives a deterministic blind PARAFAC DS-CDMA receiver with performance close to non-blind minimum mean-squared error (MMSE). The proposed PARAFAC receiver capitalizes on code, spatial, and temporal diversity-combining, thereby supporting small sample sizes, more users than sensors, and/or less spreading than users. Interestingly, PARAFAC does not require knowledge of spreading codes, the specifics of multipath (interchip interference), DOA-calibration information, finite alphabet/constant modulus, or statistical independence/whiteness to recover the information-bearing signals. Instead, PARAFAC relies on a fundamental result regarding the uniqueness of low-rank three-way array decomposition due to Kruskal (1977, 1988) (and generalized herein to the complex-valued case) that guarantees identifiability of all relevant signals and propagation parameters. These and other issues are also demonstrated in pertinent simulation experiments.

Parallel factor analysis in sensor array processing

This paper links multiple invariance sensor array processing (MI-SAP) to parallel factor (PARAFAC) analysis, which is a tool rooted in psychometrics and chemometrics. PARAFAC is a common name for low-rank decomposition of three- and higher way arrays. This link facilitates the derivation of powerful identifiability results for MI-SAP, shows that the uniqueness of single- and multiple-invariance ESPRIT stems from uniqueness of low-rank decomposition of three-way arrays, and allows tapping on the available expertise for fitting the PARAFAC model. The results are applicable to both data-domain and subspace MI-SAP formulations. The paper also includes a constructive uniqueness proof for a special PARAFAC model.

On the uniqueness of multilinear decomposition of N-way arrays

We generalize Kruskals fundamental result on the uniqueness of trilinear decomposition of three‐way arrays to the case of multilinear decomposition of four‐ and higher‐way arrays. The result is surprisingly general and simple and has several interesting ramifications. Copyright

Convex Optimization-Based Beamforming

In this article, an overview of advanced convex optimization approaches to multisensor beamforming is presented, and connections are drawn between different types of optimization-based beamformers that apply to a broad class of receive, transmit, and network beamformer design problems. It is demonstrated that convex optimization provides an indispensable set of tools for beamforming, enabling rigorous formulation and effective solution of both long-standing and emerging design problems.

Space-Time Processing for MIMO Communications

List of Contributors. Preface. Acknowledgements. 1 MIMO Wireless Channel Modeling and Experimental Characterization (Michael A. Jensen and Jon W. Wallace). 1.1 Introduction. 1.2 MIMO Channel Measurement. 1.3 MIMO Channel Models. 1.4 The Impact of Antennas on MIMO Performance. References. 2 Multidimensional Harmonic Retrieval with Applications in MIMO Wireless Channel Sounding (Xiangqian Liu, Nikos D. Sidiropoulos, and Tao Jiang). 2.1 Introduction. 2.2 Harmonic Retrieval Data Model. 2.3 Identifiability of Multidimensional Harmonic Retrieval. 2.4 Multidimensional Harmonic Retrieval Algorithms. 2.5 Numerical Examples. 2.6 Multidimensional Harmonic Retrieval for MIMO Channel Estimation. 2.7 Concluding Remarks. References. 3 Certain Computations Involving Complex Gaussian Matrices with Applications to the Performance Analysis of MIMO Systems (Ming Kang, Lin Yang, and Mohamed-Slim Alouini). 3.1 Introduction. 3.2 Performance Measures of Multiple Antenna Systems. 3.3 SomeMathematical Preliminaries. 3.4 General Calculations with MIMO Applications. 3.5 Summary. References. 4 Recent Advances in Orthogonal Space-Time Block Coding (Mohammad Gharavi-Alkhansari, Alex B. Gershman, and Shahram Shahbazpanahi). 4.1 Introduction. 4.2 Notations and Acronyms. 4.3 Mathematical Preliminaries. 4.4 MIMO System Model and OSTBC Background.8 4.5 Constellation Space Invariance and Equivalent Array-Processing-Type MIMO Model. 4.6 Coherent ML Decoding. 4.7 Exact Symbol Error Probability Analysis of Coherent ML Decoder. 4.8 Optimality Properties of OSTBCs. 4.9 Blind Decoding of OSTBCs. 4.10 Multiaccess MIMO Receivers for OSTBCs. 4.11 Conclusions. References. 5 Trace-Orthogonal Full Diversity Cyclotomic Space-Time Codes (Jian-Kang Zhang, Jing Liu, and Kon Max Wong). 5.1 Introduction. 5.2 Channel Model with Linear Dispersion Codes. 5.3 Good Structures for LD Codes: Trace Orthogonality. 5.4 Trace-orthogonal LD Codes. 5.5 Construction of Trace Orthogonal LD Codes. 5.6 Design of Full Diversity LD Codes. 5.7 Design of Full Diversity Linear Space-time Block Codes for N <M. 5.8 Design Examples and Simulations. 5.9 Conclusion. References. 6 Linear and Dirty-Paper Techniques for the Multiuser MIMO Downlink (Christian B. Peel, Quentin H. Spencer, A. Lee Swindlehurst, Martin Haardt, and Bertrand M. Hochwald). 6.1 Introduction. 6.2 Background and Notation. 6.3 Single Antenna Receivers. 6.4 Multiple Antenna Receivers. 6.5 Open Problems. 6.6 Summary. References. 7 Antenna Subset Selection in MIMO Communication Systems (Alexei Gorokhov, Dhananjay A. Gore, and Arogyaswami J. Paulraj). 7.1 Introduction. 7.2 SIMO/MISO Selection. 7.3 MIMO Selection. 7.4 Diversity and Multiplexing with MIMO Antenna Selection. 7.5 Receive Antenna Selection Algorithms. 7.6 Antenna Selection in MIMO Wireless LAN Systems. 7.7 Summary. References. 8 Convex Optimization Theory Applied to Joint Transmitter-Receiver Design in MIMO Channels (Daniel P-erez Palomar, Antonio Pascual-Iserte, John M. Cioffi, and Miguel Angel Lagunas). 8.1 Introduction. 8.2 Convex Optimization Theory. 8.3 SystemModel and Preliminaries. 8.4 Beamforming Design for MIMO Channels: A Convex Optimization Approach. 8.5 An Application to Robust Transmitter Design in MIMO Channels. 8.6 Summary. References. 9 MIMO Communications with Partial Channel State Information (Shengli Zhou and Georgios B. Giannakis). 9.1 Introduction. 9.2 Partial CSI Models. 9.3 Capacity-Optimal Designs. 9.4 Error Performance Oriented Designs. 9.5 Adaptive Modulation with Partial CSI. 9.6 Conclusions. Appendix. References. Index.

Spectrum Sharing in Wireless Networks via QoS-Aware Secondary Multicast Beamforming

Secondary spectrum usage has the potential to considerably increase spectrum utilization. In this paper, quality-of-service (QoS)-aware spectrum underlay of a secondary multicast network is considered. A multiantenna secondary access point (AP) is used for multicast (common information) transmission to a number of secondary single-antenna receivers. The idea is that beamforming can be used to steer power towards the secondary receivers while limiting sidelobes that cause interference to primary receivers. Various optimal formulations of beamforming are proposed, motivated by different ldquocohabitationrdquo scenarios, including robust designs that are applicable with inaccurate or limited channel state information at the secondary AP. These formulations are NP-hard computational problems; yet it is shown how convex approximation-based multicast beamforming tools (originally developed without regard to primary interference constraints) can be adapted to work in a spectrum underlay context. Extensive simulation results demonstrate the effectiveness of the proposed approaches and provide insights on the tradeoffs between different design criteria.

Fast and effective retrieval of medical tumor shapes

Investigates the problem of retrieving similar shapes from a large database; in particular, we focus on medical tumor shapes (finding tumors that are similar to a given pattern). We use a natural similarity function for shape matching, based on concepts from mathematical morphology, and we show how it can be lower-bounded by a set of shape features for safely pruning candidates, thus giving fast and correct output. These features can be organized in a spatial access method, leading to fast indexing for range queries and nearest-neighbor queries. In addition to the lower-bounding, our second contribution is the design of a fast algorithm for nearest-neighbor searching, achieving significant speedup while provably guaranteeing correctness. Our experiments demonstrate that roughly 90% of the candidates can be pruned using these techniques, resulting in up to 27 times better performance compared to sequential scanning.

Approximation Bounds for Quadratic Optimization with Homogeneous Quadratic Constraints

We consider the NP-hard problem of finding a minimum norm vector in

Convex approximation techniques for joint multiuser downlink beamforming and admission control

n

Kruskal's permutation lemma and the identification of CANDECOMP/PARAFAC and bilinear models with constant modulus constraints

-dimensional real or complex Euclidean space, subject to