1. Spatial Modulation for MIMO Wireless Systems

2. Machine Learning and Signal Processing in Cognitive Radios




Spatial Modulation for MIMO Wireless Systems

  • Duration:  Half Day  (3.5 hours)

Instructor(s) name:

Marco Di Renzo, Chargé de Recherche CNRS CNRS – SUPELEC – Univ. Paris-Sud XI

3 rue Joliot-Curie, 91192 Gif-sur-Yvette, France

Tel: +33 (0)1 69 85 17 36

Fax: +33 (0)1 69 85 17 65

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Harald Haas, Professor                                                                              

IDCOM, The University of Edinburgh                                                                           

Fax: +44 (0)131 650-6554                                                                         

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Ali Ghrayeb, Professor

ECE Department, Concordia University Edinburgh, EH9 3JL, UK

Montreal, Quebec, H3G 1M8 Canada Tel: +44 (0)131 650-5591                            

Tel: 1 (514) 848-2424 ext 4120

Fax: 1 (514) 848-2802

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Primary Audience:

Students, academic researchers, industry affiliates and individuals working for government, military, science and technology institutions who would like to learn more about innovative MIMO concepts for low-complexity and energy-efficient wireless communication systems, as well as their applications to emerging communication paradigms such as relay-aided, multi-user cooperation,  small  cell  cellular  networks,  and  optical  wireless.  The  tutorial  is  intended  to  provide  the  audience  with  a complete overview of the potential benefits, research challenges,  implementation  efforts and applications  to many future wireless communication systems and standards, with the inclusion of the emerging pre-standardization  activities on large- scale (“massive”) MIMO systems.

Novelty, Importance, and Timeliness:

This tutorial addresses a very recent transmission technology for MIMO wireless systems, which has been receiving for the past few years the interest of a broad research community across all continents. Hence, it is expected to draw a lot of interest from the wireless communications community from different parts of the world. Even though SM has a history that goes back to the early 2000s, it is only during the last three or four years that it has gained momentum in the wireless community, especially with the advent of “massive” MIMO and green communications concepts. The number of researchers, both from the  academia  and  industry,  working  on  SM  theory  and  applications  is currently  growing  exponentially.  These  potential attendees of the proposed tutorial will definitely benefit from the broad tutorial outline, covering state-of-the-art, applications, implementation challenges and experimental activities.


Tutorial Motivation and Relevance:

The  key  challenge  of  future  mobile  communications  research  is  to  strike  an  attractive  compromise  between  wireless network’s area spectral–efficiency and energy–efficiency. This necessitates new approaches to wireless system design, embracing the rich body of existing knowledge especially on Multiple–Input–Multiple–Output (MIMO) technologies. In the proposed tutorial, we intend to describe a new and emerging concept to wireless system design, which is conceived for single–RF large–scale MIMO communications and it is best-known as Spatial Modulation (SM). The concept of SM has established itself as a beneficial transmission paradigm, subsuming numerous members of the MIMO system–family. The research of SM has reached sufficient maturity to motivate its comparison to state–of–the–art MIMO communications, as well as to inspire its application to other emerging wireless systems such as relay–aided, cooperative, small–cell, optical wireless and  power–efficient  communications.  Furthermore,  it  has  received  sufficient  research  attention  to  be  implemented  in testbeds, and it holds the promise of stimulating further vigorous inter–disciplinary research in the years to come.

The proposed tutorial is intended to offer a comprehensive  state–of–the–art  survey on SM–MIMO research, to provide a critical  appraisal  of its potential  advantages,  and to promote  the discussion  of its beneficial  application  areas  and their research challenges leading to the analysis of the technological issues associated with the implementation of SM–MIMO. The tutorial is concluded with the description of the world’s first experimental activities in this vibrant research field.


This tutorial  is based  on the following  publication:  “M. Di Renzo,  H. Haas  A. Ghrayeb,  S. Sugiura,  L. Hanzo,  “Spatial Modulation for Generalized MIMO: Challenges, Opportunities and Implementation”,  Proceedings of the IEEE, vol 102, no. 1, pp. 56-103, Jan. 2014.

Tutorial Outline:

It is widely recognized that the Long Term Evolution Advanced (LTE-A) is the most promising physical–layer  standard of fourth generation (4G) cellular networks. The power consumption of the Information and Communication Technology (ICT) sector in the next decade will highly depend on the Energy Efficiency (EE) of this physical–layer standard. However, at the current stage, the LTE-A may be deemed to be conceived, designed and optimized based on the Spectral Efficiency (SE), with limited consideration of EE issues. In fact, especially at the physical–layer, the primary focus has been on achieving high data rates, without giving much cognizance of EE and implementation complexity. However, this approach is no longer acceptable to future cellular networks.

The LTE–A  physical–layer  standard  heavily  relies on MIMO technologies  for enhancing  the SE. MIMO communications constitute promising techniques for the design of fifth generation  (5G) cellular networks. In simple terms, the capacity of MIMO systems is proportional to min{Nt,Nr}, where Nt and Nr represent the number of transmit and receive antennas. This implies that the throughput may be increased linearly with the number of antennas. As a consequence, MIMO techniques can provide high data rates without increasing  the spectrum  utilization  and the transmit  power. However,  in practice,  MIMO systems need a multiplicity of associated circuits, such as power amplifiers, RF chains, mixers, synthesizers,  filters, etc., which substantially increase the circuit power dissipation of the Base Stations (BSs). More explicitly, recent studies have clearly shown that the EE gain of MIMO communications  increases with the number of antennas, provided that only the transmit power of the BSs is taken into account and their circuit power dissipation is neglected. On the other hand, the EE gain of MIMO communications remains modest and decreases with the number of active transmit–antennas, if realistic power consumption  models  are considered  for the BSs. These  results  highlight  that the design  of EE–MIMO  communications conceived for multi–user multi–cell networks is a fairly open research problem. In fact, many system parameters have to be


considered, such as the bandwidth, the transmit power, the number of active transmit/receive antennas, the number of active users, etc., which all contribute to the fundamental transmit power vs. circuit power dissipation and multiplexing gain vs. inter–user interference trade–offs. As a result, while the SE advantages of MIMO communications are widely recognized, the EE potential of MIMO communications for cellular networks is not well understood. Hence, new air–interface transmission techniques  have  to be developed  that are capable  of improving  SE and EE at the same  time by at least  an order  of magnitude.


Conventional MIMO communications take advantage of all the antennas available at the transmitter by simultaneously transmitting  multiple  data  streams from  all  of  them.  Thus,  all  transmit–antennas  are  active  at  any  time  instance.  By appropriately choosing the transmission/precoding  matrices, both multiplexing and transmit–diversity gains can be obtained using  MIMOs.  The  reason  behind  this  choice  is  that  simultaneously  activating  all transmit–antennas   results  in  SE optimization. Unfortunately, this choice does not lead to EE optimization. In fact, multiple RF chains at the transmitter are needed to be able to simultaneously transmit many data streams, each of them requiring an independent power amplifier that is known  to dissipate the majority  of the power  consumed  at the transmitter.  These  considerations  imply  that  a major challenge of next–generation  MIMO–aided cellular networks is the design of multi–antenna  transmission  schemes with a limited  number  of  active  RF  chains  aiming  for  reducing  the complexity,  to  relax  the  inter–antenna  synchronization requirements, and inter–channel interference, as well as the signal processing complexity at the receiver, whilst aiming for improving the EE.


In this context, single–RF MIMO design is currently emerging as a promising research field. The fundamental idea behind single–RF MIMO is to realize the gains of MIMO communications, i.e., spatial multiplexing and transmit–diversity, with the aid of many antenna–elements, of which only a few, possibly a single, activated antenna–elements (single–RF front–end) at the transmitter  at  any  modulation  instant.  The  rationale  behind  the  multi–RF  to single–RF  paradigm  shift  in MIMO  design originates from the consideration that large numbers of transmit–antennas (radiating elements) may be accommodated at the BSs (large–scale MIMO design), bearing in mind that the complexity and power consumption/dissipation of MIMO communications are mainly determined by the number of simultaneously  active transmit–antennas,  i.e., by the number of active RF chains.


Fueled by these considerations, SM has recently established itself as a promising transmission concept, which belongs to the single–RF large–scale MIMO wireless systems family, whilst exploiting the multiple antennas in a novel fashion compared to state–of–the–art  high–complexity  and power–hungry classic  MIMOs.  In simple  terms,  SM can be regarded  as a MIMO concept  that  possesses  a  larger  set  of  radiating  elements  than  the  number  of transmit–electronics.  SM–MIMO  takes advantage of the whole antenna–array at the transmitter, whilst using a limited number of RF chains. The main distinguishing feature  of  SM–MIMOs  is  that  they  map  additional  information  bits  onto  an  “SM  constellation  diagram”,  where  each constellation element is constituted by either one or a subset of antenna–elements. These unique characteristics facilitate for high–rate MIMO implementations  to have reduced signal processing and circuitry complexity, as well as an improved EE. Recent analytical and simulation studies have shown that SM–MIMOs have the inherent potential of outperforming many state–of–the–art  MIMO  schemes,  provided  that  a  sufficiently  large  number  of  antenna–elements  is  available  at  the transmitter, while just a few of them are simultaneously active.


In a nutshell, the rationale behind SM–MIMO communications design for spectral– and energy–efficient cellular networks is centered upon two main pillars: 1) Given the performance constraints, minimize the number of active antenna–elements in order to increase the EE by reducing the circuit power consumption at the transmitter (single–RF MIMO principle); 2) Given the implementation and size constraints, maximize the number of passive antenna–elements in order to increase both the SE and the EE by reducing the transmit power consumption (large–scale MIMO principle). This is realized by capitalizing on the multiplexing gain introduced by mapping additional bits onto the “SM constellation diagram”.

Detailed Outline (topics to be covered):

1. SM-MIMO: Operating Principle and Generalized Transceiver Design

    a. Short overview of MIMO wireless systems

    b. Advantages and disadvantages of MIMO, and motivation behind SM-MIMO

    c. Generalized  MIMO  transceiver  based  on  SM  (transmitter,  receiver,  spatial-  and  signal-constellation diagrams)

    d. Advantages and disadvantages of SM-MIMO (single-RF, single-stream decoding, low-complexity “massive”

implementation, etc.)

2. SM-MIMO: A Comprehensive Survey

    a. Historical perspective

    b. State-of-the-art on transmitter design

    c. State-of-the-art on receiver design

    d. State-of-the-art on transmit-diversity and space-time-coded SM-MIMO

    e. State-of-the-art on performance and capacity analysis over fading channels

    f.  State-of-the-art on performance and design in the presence of multiple-access interference

    g. State-of-the-art on robustness to channel state information at the receiver

3. SM-MIMO: Application Domains Beyond the PHY-Layer

    a. Distributed/network implementation of SM-MIMO

    b. Application to relaying-aided and cooperative wireless networks

    c. Application to green networks: “Massive” SM-MIMO design and the GreenTouch initiative

    d. Application to heterogeneous cellular networks

    e. Application to visible light communications

4. SM-MIMO: Research Challenges and Opportunities

    a. Open physical–layer research issues

    b. Appraising the fundamental trade–offs of single– vs. multi–RF MIMO designs

    c. Large–scale implementations: Training overhead for CSIT/CSIR acquisition

    d. From single–user point–to–point to multi–user multi–cell SM–MIMO communications

    e. Millimeter–wave communications: The need for beamforming gains

    f.  Small cell heterogeneous cellular networks: Towards interference engineering

    g. Radio frequency energy harvesting: Taking advantage of the idle antennas

    h. Leveraging the antenna modulation principle to a larger extent

5. SM-MIMO: From Theory to Practice - Experimental Results and Channel Measurements from a Testbed Platform

    a. Description of the hardware testbed

    b. Description of the measurements campaign

    c. Real-world performance results and comparison with state-of-the-art MIMO

Bios of the Presenters

Marco Di Renzo (SM’05-AM’07-M’09) received the Laurea (cum laude) and the Ph.D. degrees in Electrical and Information Engineering from the Department of Electrical and Information Engineering, University of L’Aquila, Italy, in April 2003 and in January 2007, respectively. In October 2013, he received the Habilitation à Diriger des Recherches (HDR) degree majoring in Wireless Communications Theory, from the University of Paris-Sud XI, France. From August 2002 to January 2008, he was with the Center of Excellence for Research DEWS, University of L’Aquila, Italy. From February 2008 to April 2009, he was a Research Associate with the Telecommunications Technological Center of Catalonia (CTTC), Barcelona, Spain. From May 2009 to December 2009, he was an EPSRC Research Fellow with the Institute for Digital Communications (IDCOM), The University of Edinburgh, Edinburgh, United Kingdom (UK). Since January 2010, he has been a Tenured Academic Researcher (“Chargé de Recherche Titulaire”) with the French National Center for Scientific Research (CNRS), as well as a faculty  member  of  the  Laboratory  of  Signals  and  Systems  (L2S),  a  joint  research  laboratory  of  the  CNRS,  the  Ecole Supérieure d’Electricité (SUPELEC), and the University of Paris-Sud XI, Paris, France. His main research interests are in the area of wireless communications theory, signal processing, and information theory. Dr. Di Renzo is the recipient of a special mention   for   the   outstanding   five–year   (1997–2003)   academic   career,   University   of   L’Aquila,   Italy;   the   THALES Communications fellowship for doctoral studies (2003–2006), University of L’Aquila, Italy; the Best Spin–Off Company Award (2004),  Abruzzo  Region,  Italy;  the  Torres  Quevedo  award  for  research  on  ultra  wide  band  systems  and  cooperative localization   for  wireless   networks   (2008–2009),   Ministry   of  Science   and  Innovation,   Spain;   the  “Dérogation   pour l’Encadrement de Thèse” (2010), University of Paris–Sud XI, France; the 2012 IEEE CAMAD Best Paper Award from the IEEE  Communications  Society;  the  2012  Exemplary  Reviewer  Award  from  the  IEEE  WIRELESS  COMMUNICATIONS LETTERS of the IEEE Communications Society; the 2013 IEEE VTC-Fall Student Best Paper Award from the IEEE Vehicular Technology Society for the paper entitled "Performance of Spatial Modulation using Measured Real-World Channels"; the 2013 NoE-NEWCOM# Best Paper Award; the 2013 Top Reviewer Award from the IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY of the IEEE Vehicular Technology Society; the 2013 IEEE/COMSOC Best Young Researcher Award for the EMEA Region; and the 2014 IEEE ICNC Best Paper Award for the IEEE Wireless Communications Symposium of the IEEE


Communications Society for the paper entitled " Performance Analysis of Spatial Modulation MIMO in a Poisson Field of Interferers”. He currently serves as an Editor of the IEEE COMMUNICATIONS  LETTERS and of the IEEE TRNSACTIONS ON COMMUNICATIONS (Heterogeneous Networks Modeling and Analysis).


Harald Haas (SM’98-AM’00-M’03) holds the Chair of Mobile Communications in the Institute for Digital Communications (IDCOM) at the University of Edinburgh. He is co-founder and part-time CTO of a university spin-out company pureVLC Ltd. His main research interests are in the areas of wireless system design and analysis as well as digital signal processing, with a particular focus on interference coordination in wireless networks, spatial modulation, and optical wireless communication. Professor Haas holds more than 23 patents. He has published more than 60 journal papers including a Science Article and more than 170 peer-reviewed conference papers. Nine of his papers are invited papers. He has co-authored a book entitled “Next Generation Mobile Access Technologies: Implementing TDD” with Cambridge University Press. Since 2007, he has been a Regular High Level Visiting Scientist supported by the Chinese “111 program” at Beijing University of Posts and Telecommunications (BUPT). He was an invited speaker at the TED Global conference 2011. He has been shortlisted for the World Technology Award for communications technology (individual) 2011. He is Associate Editor of IEEE TRANSACTIONS ON COMMUNICATIONS. He has been chair and co-chair of the Optical Wireless Communications (OWC) workshop at Globecom  2011  and 2012  respectively.  He recently  has been  awarded  the EPSRC  Established  Career  Fellowship.  He recently received the 2013 IEEE VTC-Fall Student Best Paper Award from the IEEE Vehicular Technology  Society. The paper is entitled "Performance of Spatial Modulation using Measured Real-World Channels".


Ali Ghrayeb (S’97-M’00-SM’06) received the Ph.D. degree in electrical engineering from the University of Arizona, Tucson, USA in 2000. He is currently a Professor with the Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada. He is a co-recipient of the IEEE Globecom 2010 Best Paper Award. He holds a Concordia University Research Chair in Wireless Communications. He is the co-author of the book “Coding for MIMO Communication Systems” (Wiley, 2008). His research interests include wireless and mobile communications, error correcting coding, MIMO systems, wireless  cooperative  networks,  and  cognitive  radio  systems.  Dr. Ghrayeb  has instructed/co-instructed  technical  tutorials related to MIMO systems at several major IEEE conferences, including ICC, Globecom, WCNC and PIMRC. He served as a co-chair of the Communications  Theory Symposium  of IEEE Globecom 2011, Houston, Texas. He also served on many technical committees of IEEE conferences in different capacities. He serves as an Editor of the IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, the IEEE TRANSACTIONS ON COMMUNICATIONS, and the Physical Communications Journal. He served as an Editor of the IEEE TRANSACTIONS ON SIGNAL PROCESSING, an Associate Editor of the IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY and the Wiley Wireless Communications and Mobile Computing Journal.




 Machine Learning and Signal Processing in Cognitive Radios

Spatial Modulation for MIMO Wireless Systems

Duration:  Half Day  (4 hours)

Instructor(s) name: 

Dr. Sudharman K. Jayaweera, Associate Professor

Department of Electrical and Computer Engineering
University of New Mexico

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505 277 1078 (phone), 505 277 1439 (fax).

Address: MSC01 1100, ECE Department, 1 University of New Mexico, Albuquerque, NM 87131-0001.

Motivation and Objectives:

Future wireless communication systems will undoubtedly be based on Cognitive radios (CR) in some form or fashion. Cognitive radios are an evolution of software-defined radios (SDR). While SDR’s can be intelligent radios, what sets cognitive radios apart from SDR is their ability to learn and be self-aware. Thus, a cognitive radio architecture must necessarily consist of modules that support these functionalities.


Self-awareness is achieved through spectrum knowledge acquisition, in part called the spectrum sensing. The role of signal processing and machine learning in spectrum knowledge acquisition and subsequent use of such acquired knowledge in decision-making and radio reconfiguration cannot be over- emphasized. In fact, one may argue that it is signal processing and machine learning that give rise to a cognitive and intelligent radio. These algorithms form the brain and brain functions of a cognitive radio while an SDR platform acts as the body of the radio. It is a timely topic to emphasize this aspect of cognitive radios and this tutorial is motivated by that.


The objective of this tutorial is to discuss cognitive radios from the perspectives of signal processing and machine learning. This emphasize will be used to highlight the potential of cognitive radio technology to go far beyond what has perhaps been considered so far in literature. Signal processing and machine learning solutions needed to realize this full potential of cognitive radios will be identified and candidate techniques will be discussed. A discussion of limitations of existing signal processing and machine learning techniques to meet the challenges posed by cognitive radios is expected to stimulate further research on these topics.



This tutorial discusses cognitive radios from the perspective of signal processing. A cognitive radio is defined as a “self-aware and self-reconfigurable SDR capable of learning”. A functional architecture that supports these capabilities will be introduced and role of signal processing and machine learning will be identified. The tutorial will be focused on particular signal processing problems that are unique to cognitive radios, in particular those that are associated with spectrum knowledge acquisition: Wideband spectrum scanning, wideband and robust spectrum sensing, compressive sensing, spectral activity detection, signal classification and identification. The unique aspects of these problems in the context of cognitive radios will be emphasized in order to appreciate the challenges to be overcome. Limitations of existing signal processing and machine learning approaches to meet these challenges will be pointed out before discussing in detail both classical statistical signal processing solutions and novel techniques that incorporate machine learning algorithms.


Detailed Outline:

1.   The Cognitive Radio

a.   Cognitive radio concept

b.   The functional model of a cognitive radio

c.   Cognitive radio architecture

2.   Cognitive radios and dynamic spectrum sharing

a.   Interference and spectrum opportunities

b.   Dynamic spectrum access

c.   Dynamic spectrum leasing

3.   Signal processing challenges in cognitive radios

4.   Wideband Spectrum Sensing problem

5.   Wideband spectrum scanning

a.   Spectrum segmentation and sub-banding

b.   Sub-band selection problem

6.   Spectral activity detection

a.   Optimal wideband spectral activity detection

b.   Compressive sampling

c.   Wavelet-based wideband spectral activity detection

d.   Robust wideband spectrum sensing

7.   Signal Classification and Identification

a.   Signal classification problem in a wideband cognitive radio

b.   Feature extraction

c.   Parametric and non-parametric signal classifiers

d.   Bayesian non-parametric signal classification in cognitive radios

8.   Machine Learning in cognitive radios

a.      Artificial neural networks

b.   Support vector machines

c.      Reinforcement learning

d.   Multi-agent learning (game-theoretic and cooperative)

9.   Cognitive radios and future of wireless communications

10. Concluding Remarks

Primary Audience:

Students, researchers, program managers, engineers/scientists and individuals working for military, government and technology institutions who desire to learn about cognitive radios, their potential and keys to realizing their potential through signal processing and machine learning. The tutorial will give a comprehensive understanding of machine learning theory in a unified setting that will highlight its relevance and applications to cognitive radios and cognitive radio networks. The tutorial will highlight both promise of existing machine learning techniques as well as their limitations, in the context of cognitive radios, helping to calibrate research expectations and encourage further research on learning techniques specifically suitable for cognitive radios and networks.


Graduate students will particularly benefit by attending this tutorial due to the opportunity to see how adaptive and learning techniques developed in widely differing fields have come to form the so-called machine learning. The tutorial will highlight the underlying similarities and differences among different learning mechanisms helping the audience to see them all in a unified setting. The discussion on limitations of existing learning techniques when applied to cognitive radios will encourage further research on machine learning approaches for cognitive radios.

Biography of the Presenter

Sudharman K. Jayaweera (S’00, M’04, SM’09) received the B.E. degree in Electrical and Electronic Engineering with First Class Honors from the University of Melbourne, Australia, in 1997 and M.A. and PhD degrees in Electrical Engineering from Princeton University in 2001 and 2003, respectively. He is an associate Professor at the Department of Electrical and Computer Engineering at University of New Mexico in Albuquerque, USA where he currently serves as the Associate Chair and the Director of the Graduate Program. A senior member of IEEE, Dr. Jayaweera currently serves as an Editor of IEEE Transactions on Vehicular Technology. He was an Associate Editor of EURASIP Journal on Advances in Signal Processing from 2003-2013. Dr. Jayaweera has been involved in several conference organizations committees including most recently as the Publicity Chair of the 10th International Workshop on Broadband Wireless Access (BWA) in conjunction with the 2014 IEEE Globecom in Austen, TX (Dec. 2014), as the Tutorials and Workshop Chair of the at the IEEE Vehicular Technology Conference (VTC2013-Fall) in Las Vegas, NV (Sep. 2013), and as the General chair of the First International Workshop on Wideband Mobile cognitive Radios (WMCR) at the 2013 Fall IEEE Vehicular Technology Conference (VTC Fall), among others.


Dr. Jayaweera is the author of an upcoming Wiley book on cognitive radios and the co-author of a recent survey on machine learning techniques in cognitive radios appeared in IEEE Communications Surveys and Tutorials.


Among his honors are two best paper awards at IEEE international conferences and the AFRL Faculty Fellowship. His current research interests include cooperative and cognitive communications, information theory of networked-control systems, statistical signal processing and wireless sensor networks. His research in cognitive radios has specifically been focused on distributed decision-making, learning and cooperative communications aspects. In June 2010 he taught a short summer course on Cognitive Radio Networks at the University of Vigo in Spain. He was an invited panel member in the panel discussion at the First International Workshop on Cognitive radio and Cooperative strategies for POWER saving (C2POWER) collocated with the 6th International Mobile Multimedia Communications Conference (MOBIMEDIA'10) held in Lisbon, Portugal (, and is scheduled to deliver the plenary talk titled “Cognitive Radios: What Are they Really?” at the IEEE Radio and Wireless Symposium (RWS’2011) in Phoenix, AZ in January, 2011 (



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Kien T. Truong

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