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Tutorials offered at VTC2011-Spring

All tutorials will be held on Sunday May 15, 2011.

  Tutorial Name Presented by Time Room
T1Towards Holistic Green Communications and NetworkingDr. Konstantinos Samdanis and Dr. Dominique Dudkowski, NEC Europe Ltd.08:30–12:00TBA
T2Cognitive radio based on UWB technology - a perfect binomialA. Giorgetti (Univ. of Bologna), S. Kandeepan (Create-Net), and L. De Nardis, (La Sapienza Univ.) 08:30–12:00TBA
T3Participatory Sensing: Crowdsourcing Data from Mobile Smartphones in Urban SpacesSalil Kanhere, University of New South Wales, Sydney, Australia08:30–12:00Corso A
T4Cooperative active and passive localization and tracking: fundamental limits and UWB case studyDavide Dardari (University of Bologna, Italy), Andrea Conti (University of Ferrara, Italy) 13:30–17:00TBA
T5Low-Complexity Algorithms for Large-MIMO DetectionProf. A. Chockalingam, Department of ECE, Indian Institute of Science, Bangalore, India13:30–17:00TBA
T6Mobility models and social networksPaolo Santi, Istituto di Informatica e Telematica del CNR, Pisa, Italy13:30–17:00Corso A



T1: Towards Holistic Green Communications and Networking
Presented by: Dr. Konstantinos Samdanis and Dr. Dominique Dudkowski, NEC Europe Ltd.
Time: 08:30–12:00
Room: TBA



Tutorial Objectives

Tutorial Outline

    Introduction, motivation, and preliminaries (45 min)
      Origins and relevance of energy saving considerations
      Traditional energy saving domains of wireless sensor and ad-hoc networks
      Figures/numbers on energy consumption; projections, trends and energy consumption scenarios based on the most recent studies
      System model and taxonomy of energy consumption, including consumers, cooling infrastructure, and power distribution infrastructure
      Impact of energy consumer subsystems on each other
      Rebound effect (Kazzoom-Brookes postulate)
      Relation of energy efficiency to network performance
      Energy efficiency in planning and operation, OPEX and CAPEX
      Powering and analysis of (alternative) energy sources and batteries
      Identification of energy consumers in wired and wireless networks
      Hardware and devices, network stack, software, protocols and services
      Notion and role of energy proportionality
      Distinction of performance states versus power states
      Energy consumption models of network elements
    Taxonomy of energy efficiency principles in wired and wireless networks with classification of the state of the art (90 min)
      Distributed, centralized, cooperative, and self-organization energy management paradigms
      Energy-efficient network design / planning / topology / cognitive radio
      Power state techniques (e.g. link and switch, radio specific sleep modes)
      Dynamic resource scaling (e.g. link rate adaptation, BS carrier scaling)
      Packet scheduling techniques (e.g. bursts, synchronization), DTN
      M2M and Internet of things energy considerations
      Flow aggregation in fixed networks, load concentration on specific BSs.
      Protocol proxying, information/function placement and offloading
      Energy modeling of communication networks
      Energy consumption measurement and monitoring, and applications
      Analysis of compatibility and contrariness of energy saving approaches
    Key energy saving use cases (45 min)
      Energy saving and load balancing paradigm in cellular radio networks
      Energy saving in cloud computing/network infrastructures
    Towards holistic green communications and networking (30 min)
      Enabling principles and technologies for holistic energy management
      Joint energy management approaches for partially holistic systems
      Holistic energy management policies and network performance
      Energy efficiency between access and core networks, and between wired and wireless system domains
      Overview of national and international project landscape
      Standardization requirements towards holistic energy management
      Vision: intrinsic holistic energy efficient communication networks

Primary Audience
Our tutorial considers the most up-to-date techniques for energy efficient communications and networking providing a quick technical introduction suitable for researchers, practitioners and standardization engineers with a specific interest in the topic. It presents the socio-economic aspects as well as the current state of the art and derives the main technical principles providing a comprehensive holistic approach to energy efficient communications and networking.

This tutorial brings the fundamental technical aspects from academia and industry aiming to provide a quick and comprehensive introduction into the topic of energy saving in ICT. Beyond its technical nature, it is the first that introduces a standards perspective from both wireless, i.e. 3GPP and the Internet, i.e. IETF point of view, bringing a unique value of how energy saving technologies may be deployed in networks and finally examines the holistic energy saving insights.


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T2: Cognitive radio based on UWB technology - a perfect binomial
Presented by: A. Giorgetti (Univ. of Bologna), S. Kandeepan (Create-Net), and L. De Nardis, (La Sapienza Univ.)
Time: 08:30–12:00
Room: TBA


Studies have shown that the spectrum is under utilized in the frequency, time and spatial domains in several licensed frequency bands. Cognitive Radio (CR) is proposed and encouraged especially by the radio regulatory bodies around the world as a solution to increase the efficiency of the spectral usage by opportunistically re-utilize spectrum already allocated. Ultra wideband (UWB) technology, in particular, is a potential candidate for the deployment of cognitive radio (CR) systems, given its implicit need for coexistence. Moving from this premise, this tutorial will identify the requirements and open research issues for the development of CR networks, and will address them in the specific case of UWB technology. To this aim, the tutorial will introduce the key characteristics of UWB communication systems, focusing on industrial standards (including both short distance, high data rate devices and low rate devices with ranging capabilities) and identifying application scenarios for UWB-CR networks.
The tutorial will first introduce the concept of Software Defined Radios (SDR) and the corresponding architecture enabling to have intelligence (cognition) in the radio in order to operate as a CR. The tutorial will then cover the problem of analyzing the external environment and adapting to it: spectrum sensing techniques, learning algorithms and generation of environment-related information, in the form of Radio Environment Maps (REM) will be addressed. Furthermore, the requirements posed on the UWB-CR wireless systems in the creation of REMs will be defined, with specific focus on the combination of positioning capabilities and detection of legacy or primary users.

Tutorial Objectives
The tutorial introduces the key aspects (and the corresponding research issues) in the design of a Cognitive Radio (CR) system: Legacy User Classification, Signal Characteristics, Spectrum Sensing and Detection Techniques, Coexistence analysis and interference mitigation techniques, Localizing and Tracking Interferers, and Radio Environment Mapping.
Introduces the most important worldwide CR-related standardization activities such as the IEEE 1900 group, ETSI Technical Committee on Reconfigurable Radio Systems (RRS), the SDR Forum and the IEEE 802.22 groups.
Introduces the Ultra Wide Band (UWB) technology and the main characteristics of the industrial standards based on it, and illustrate the opportunities offered by the adoption of UWB as the enabling technology for the development of CR networks, as well as the potential application scenarios and coexistence requirements.
Illustrates the issues posed by the adoption UWB in CR in the specific field of sensing and detecting legacy or primary users: performance of spectrum sensing with a UWB receiver, impact of cooperation, and possibility of identifying narrowband and wideband interfering systems.
Presents the possible techniques for mitigating mutual interference between secondary UWB-CR systems and primary users: spectrum shaping through appropriate selection of modulation schemes, coding schemes and pulse shapes for low rate impulsive systems, specific OFDM-based spectrum shaping for high rate systems, Detect-And-Avoid solutions.
Introduces the concept of Radio Environment Maps (REMs) and the advantages guaranteed by the adoption of REMs in UWB-CR systems.
Presents research issues and possible solutions for the creation of REMs: definition of requirements on the availability of position information to terminals in the UWB-CR network, and introduction to the potential solutions for the retrieval of such position information: distance estimation (ranging) techniques, centralized positioning algorithms, distributed anchor-based positioning algorithms and distributed anchor-free positioning algorithms.
Finally, the tutorial will addresses existing approaches and highlight open issues and challenges in UWB-based cognitive radio.

Tutorial Outline

    Introduction to Cognitive Radios
      Introduction to the Tutorial
      Introduction to software defined radios (SDR), architecture, and Cognitive Radios
      ETSI - TC RRS based SDR architecture and design
    UWB: Candidate for Cognitive Radio
      What is UWB technology, a brief introduction
      UWB based CR: challenges, opportunities and application scenarios
      MB-OFDM UWB: the WiMedia standard
      Impulsive UWB: the IEEE 802.15.4a standard
      UWB-specific physical layer coexistence techniques: pulse and spectrum shaping
    Spectrum Users and Spectral Occupancy
      The UWB frequency range and co-existing radios (legacy users)
      Stochastic analysis of legacy user radio signals
      Stochastic traffic models, spectral occupancy models of legacy users and the evolution of spectral holes
    Spectrum Sensing Techniques and Dynamic Spectrum Access
      Spectrum sensing methods and techniques (energy/cyclostationary feature based detection)
      Cooperative spectrum sensing (using multiple CR nodes)
      Spectrum sensing by linear frequency sweeping for UWB-CR
      Cluster based cooperative spectrum sensing techniques for UWB-CR
      Periodic spectrum sensing techniques and performance analysis
      Case study for detecting (IEEE 802.11n) legacy users by UWB-CR
    Coexistence and interference analysis
      Introduction to coexistence between UWB and legacy users
      Coexistence analysis as a first step for an efficient spectrum usage
      Impact of a legacy user to UWB-CR and vice-versa for both Impulse Radio UWB as well as OFDM-based UWB
      Coexistence in a heterogeneous network scenario: the effect of aggregate interference
    Interference Mitigation Techniques
      Interference mitigation techniques by DAA (detection-and-avoidance) cognitive radio principles
      Suppression of interference in OFDM and Impulse Radio UWB-CR
      Spectrum-agile UWB-CR waveforms generation
    Localization and Tracking Legacy User Radios
      Distributed vs. centralized positioning in wireless systems
      Positioning algorithms (anchor based and anchor-free)
      Positioning in the UWB context
      Introduction to Radio Environment Mapping (REM)
    Conclusion and Open Research Problems

Primary Audience
Intended audience for the tutorial are experts and scholars in the broad field of wireless communications interested in getting a better understanding of the issues related to Cognitive Radio design and to the application of UWB technology to the Cognitive Radio Paradigm. The Tutorial will be organized so to allow a wide audience to take advantage from its content, ranging from graduate students to engineers and expert researchers willing to start working in the field of Cognitive Radio and UWB.

The tutorial is, to authors' best knowledge, the first to focus on state-of-the-art and research issues for Cognitive Radio deployment in the specific case of UWB as the key enabling technology. As such, the tutorial moves away from a generic introduction to Cognitive Radio by focusing on specific issues and opportunities related to the development of CR networks based on UWB, taking into account physical layer and MAC characteristics of UWB standards.

Andrea Giorgetti (MIEEE'04) received the Ph.D. degree from the University of Bologna, Italy in 2003. Since 2006 he is an A/Professor at the II Engineering Faculty, Department of Electronics, Computer Science and Systems (DEIS) at the University of Bologna. Since 2006 he is Research Affiliate at the Massachusetts Institute of Technology (MIT), Cambridge, USA, working on the ultra-wide bandwidth technology.
He was Co-chair of the Wireless Networking Symposium at the IEEE Int. Conf. on Commun. (ICC 2008), Beijing, CHINA, May 2008, and Co-chair of the MAC track of the IEEE Wireless Comm. & Networking Conf. (WCNC 2009), Budapest, Hungary, Apr 2009. He was co-recipient of the best student paper award at the IEEE International Conference on Ultra-Wideband (ICU), held in Waltham, Massachusetts, Sept. 2006. His research interests include ultra-wideband communications and radar, wireless sensor networks and multiple-antenna-systems.

Sithamparanathan Kandeepan (MIEEE'03, SMIEEE'09) received his PhD from the University of Technology, Sydney in 2003. He is currently a Senior Researcher and leads the Cognitive Information Networks (CoIN) group at the Create-Net Research Centre, Italy. He was awarded the 'Earth Station Satellite Fellow' award to conduct his PhD degree at UTS with the CRCSS on the Fedsat project. He has presented many IEEE lectures in the areas of cognitive radios at University of New Mexico and Ryerson University.

Luca De Nardis (MIEEE'04) received his PhD from the University of Rome La Sapienza in 2005. Since 2008 he is an A/Professor at the INFO-COM department. In 2005/2006 he was a visiting-scholar at the Berkeley Wireless Research Center, University of California Berkeley. He also worked as postdoctoral fellow at the same institution in 2006/2007. His research interests focus on UWB, ad-hoc networks organization, MAC, positioning and routing protocols.

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T3: Participatory Sensing: Crowdsourcing Data from Mobile Smartphones in Urban Spaces
Presented by: Salil Kanhere, University of New South Wales, Sydney, Australia
Time: 08:30–12:00
Room: Corso A


The recent wave of sensor-rich, Internet-enabled, smart mobile devices such as the Apple iPhone has opened the door for a novel paradigm for monitoring the urban landscape known as participatory sensing. Using this paradigm, ordinary citizens can collect multi-modal data streams (e.g., audio, video, sound, location coordinates, etc) from the surrounding environment using their mobile devices and share the same using existing communication infrastructure (e.g., 3G service or WiFi access points). The data contributed from multiple participants can be combined to build a spatiotemporal view of the phenomenon of interest and also to extract important community statistics. Given the ubiquity of mobile phones and the high density of people in metropolitan areas, participatory sensing can achieve an unprecedented level of coverage in both space and time for observing events of interest in urban spaces. This tutorial will provide a comprehensive overview of this exciting new sensing paradigm and discuss the associated research challenges.

Tutorial Objectives
obtain a comprehensive overview of the participatory sensing paradigm
learn about several innovative applications based on this novel paradigm
understand system architectures and system design issues
get acquainted with the key research challenges including (a) incentives, (b) data quality (c) context and activity inference (d) signal reconstruction strategies (e) data trustworthiness (f) privacy issues
learn about future directions and open research problem

Tutorial Outline

    Overview of Participatory Sensing Applications
    System Architecture and Design Issues
    Research Challenges
      Data Quality
      Context and Activity Inference
      Reconstruction and Interpolation Strategies
      Trust and Privacy Issues
    Conclusions and Future Work

Primary Audience
This tutorial is open to researchers, academics, students and practitioners working in wireless communication and mobile computing research. It does not assume that the attendees require any prior knowledge other than basics of computer networks.

Given the widespread adoption of smart phones, participatory sensing is set to be one of the key emerging areas of research in the next few years. The tutorial offers the attendees of VTC an opportunity to gain an in-depth overview of this exciting field of research. To the best of my knowledge, a tutorial on this topic has not yet been proposed at other conferences.

Dr. Salil Kanhere received his M.S. and Ph.D. degrees, both in Electrical Engineering from Drexel University, Philadelphia in 2001 and 2003, respectively. He is currently a Senior Lecturer in the School of Computer Science and Engineering at the University of New South Wales in Sydney, Australia. His current research interests include participatory sensing, vehicular communication and wireless mesh and sensor networks. He has published over 75 peer-reviewed articles on these research topics. He has served on the organizing committee of a number of IEEE and ACM international conferences (e.g,, ACM SenSys, IEEE LCN, ACM MSWiM,, IEEE SenseApp, ACM IWCMC, ISSNIP). He is active on the program committee of numerous well-known conferences (e.g., IEE LCN, IEEE DCOSS, IEEE ICC, IEEE GLOBECOM, IEEE WCNC, etc). He currently serves as the Area Editor for the ICST Journal on Ubiquitous Environments.

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T4: Cooperative active and passive localization and tracking: fundamental limits and UWB case study
Presented by: Davide Dardari (University of Bologna, Italy), Andrea Conti (University of Ferrara, Italy)
Time: 13:30–17:00
Room: TBA


In this tutorial, the theoretical fundamental limits in ranging and active/passive localization based on the UWB technology, as well as practical schemes, will be explained. The main ranging/positioning sources of errors such as multipath, clock offsets and interference will be illustrated. Some results derived from measured data in real environments will be shown to investigate the effect of system parameters on ranging, localization, and tracking accuracy. Some possible localization and tracking algorithms will be discussed and their implementation in a real test bed shown as case studies. Finally, some advanced issues such as cooperative localization and cognitive ranging will be discussed.

Tutorial Objectives

Tutorial Outline

      The tutorial will cover relevant topics including:
      - Active and passive positioning basics
      - Ranging: time-based, time difference-of-arrival (TDOA), received signal strength (RSS)
      - Error sources in ranging: multipath channel, interference, excess delay, NLOS propagation
      - Theoretical performance limits on time-of-arrival (TOA) estimation in AWGN and in the presence of multipath
      - Practical TOA estimators in realistic conditions: the effect of multipath, interference and bandwidth
      - Localization and tracking algorithms
      - Cooperative vs non-cooperative localization
      - Anti-intruder multi-static radar systems
      - Case studies on localization and tracking

Primary Audience
The tutorial is intended for all those researchers as well as system engineers who want to have a systematic overview on short-range ranging, localization and tracking.

This tutorial touch both theoretical and experimental aspects of cooperative localization and tracking. Results are based to experimental data and provide insights for system designer.


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T5: Low-Complexity Algorithms for Large-MIMO Detection
Presented by: Prof. A. Chockalingam, Department of ECE, Indian Institute of Science, Bangalore, India
Time: 13:30–17:00
Room: TBA


This tutorial will address the challenging issue of detection complexity in realizing high spectral efficiency MIMO systems with large number (tens) of antennas (referred to as large-MIMO systems). Complexity of optimal signal detection grows exponentially in number of antennas. We will present detection algorithms that achieve near-optimal performance in large-MIMO systems at practically affordable complexities (e.g., polynomial/linear complexities in number of antennas).

Interestingly, certain algorithms rooted in machine learning/artificial intelligence show increasingly closer to optimum performance for increasing number of dimensions. We will illustrate that this large-dimension behavior of these algorithms can be exploited in large-MIMO detection. Detailed exposition of several such algorithms, their bit error performances and complexities, and comparison with other widely known detection algorithms will be the focus in this tutorial.

Specifically, algorithms based on local search heuristics, including likelihood ascent search (LAS) and reactive tabu search (RTS) algorithms, that achieve near maximum likelihood (ML) performance with large number of antennas will be presented. We will also present algorithms that achieve near maximum aposteriori (MAP) performance in large dimensions. They are based on probabilistic data association (PDA), belief propagation (BP) on Markov random fields/factor graphs, and Markov Chain Monte-Carlo (MCMC) methods. We will show that these algorithms are attractive for 16x16, 32x32, 64x64 MIMO systems with 4-QAM/16-QAM/64-QAM. Feasibility of such algorithms and growing maturity in compact antennas design can enable large-MIMO implementations, for applications including Wireless HDTV.

Tutorial Objectives
A key objective in this tutorial is to introduce the participants to the practical feasibility of near-optimal detection in MIMO systems with large number (tens) of antennas at practically affordable complexities.

Other objectives include:

1) to motivate the practical relevance of large-MIMO systems; it has been theoretically predcited and well known that MIMO channel capacity increases with the minimum of the number of Tx and Rx antennas, and therefore MIMO systems with large number of Tx/Rx antennas are of interest because of their promise of high spectral efficiency advantage.

2) to highlight the technological challenges in implementing large-MIMO systems; antenna placement issues, multiple RF/IF chains, spatial correlation among antenna elements, detection, estimation of large number of channel coefficients.

3) to highlight some recent developments in compact antenna designs suited for large-MIMO systems.

4) to illustrate that to overcome the complexity bottleneck in large-MIMO detection, algorithms rooted in machine learning/aritifical intelligence can be gainfully exploited.

5) to present specific detection algorithms based on metaheuristics, message passing on Markov random Fields and factor graphs, and Gibbs sampling that have been recently shown to be well suited for achieving near-optimal large-MIMO detection.

6) to present a detailed performance and complexity characterization of the above algorithms in large-MIMO settings.

7) to present a detailed comparison of the performance and complexity of the above algorithms with those of other widely known algorithms in the literature.

8) to illustrate that channel estimation can be done through iterative detection/channel estimation in large-MIMO systems and present supporting algorithms and simulation results.

9) to illustrate that with outer coding (e.g., turbo) and soft decision decoding, near-capacity performance can be achieved in large-MIMO systems.

Tutorial Outline

    1. Introduction
    Capacity of MIMO wireless channels is known to increase linearly with the minimum of the number of transmit and receive antennas. Very high spectral efficiencies can be achieved if large number of antennas are employed. A key challenge in practically realizing large-MIMO systems with tens of antennas is the detection complexity at the receiver. Optimal signal detection is known to grow exponentially in number of dimensions (number of antennas in MIMO). While it is known that large-dimension problems are hard, `concentration results,' which assert that, with high probability, certain large dimensional quantities are close to their means, can be exploited to obtain tractable solutions. Randomization in the algorithms can help significantly in large dimension problems. Also, techniques for dimension reduction which project the data to a lower dimension space but preserve some properties can be of much use. Tools/algorithms from artificial intelligence/machine learning are particularly amenable to large dimension problems.
    Recently, large-MIMO systems have attracted increased research attention. This is because certain algorithms from machine learning/artificial intelligence have been shown recently to achieve near-optimal performance in large-MIMO systems with tens of antennas at low complexities. In parallel, there is growing maturity in compact antennas design and RF technologies towards large number of antenna elements. These developments can enable practical implementations of large-MIMO systems, for applications including Wireless HDTV.
    This tutorial will focus on low-complexity MIMO detection algorithms that achieve near-optimal performance in large dimensions. Specifically, algorithms based on local search heuristics, including likelihood ascent search (LAS) and reactive tabu search (RTS) algorithms and their variants, that achieve near maximum likelihood (ML) performance with large number of antennas will be presented. We will also present algorithms that achieve near maximum a posteriori (MAP) performance in large dimensions. They are based on probabilistic data association (PDA), belief propagation (BP) on Markov random fields/factor graphs, and Markov Chain Monte-Carlo (MCMC) methods. An interesting aspect about these algorithms is that they achieve near-optimal performance at low complexities; whereas other widely known algorithms loose out either in performance (e.g., ZF, MMSE, ZF/MMSE-SIC) or in complexity (e.g., sphere decoding and its variants). This tutorial will dwell in detail about these large-MIMO detection algorithms, their performances, complexities, and comparison with other algorithms.
    2. Algorithms to be Covered
    The various algorithms that will be covered in this tutorial include:
      1) LAS algorithm and variants
      2) RTS algorithm and variants
    These algorithms are local search heuristics based algorithms, and they achieve near-ML performance in large dimensions.
      3) Probabilistic Data Association (PDA) algorithm
      4) Belief Propagation (BP) based algorithms and variants
      5) Markov Chain Monte-Carlo (MCMC) based algorithms
    These algorithms are iterative algorithms that achieve near-MAP performance in large dimensions.
    Bit error performance and complexities (in terms of number of real operations per symbol) of these algorithms evaluated through extensive simulations will be presented and compared.
    2.1. Tabu Search
      Tabu search, a heuristic originally designed to obtain approximate solutions to combinatorial optimization problems has been applied in communication problems. For example, design of constellation label maps to maximize asymptotic coding gain has been formulated as a quadratic assignment problem (QAP), which is solved using RTS. RTS approach has also been shown to be effective in terms of BER performance and efficient in terms of computational complexity in CDMA multiuser detection. In this tutorial, we will demonstrate that RTS and its variants are quite powerful techniques for large-MIMO detection.
    2.2. Belief Propagation (BP)
      BP is a technique that solves inference problems using graphical models. More precisely, BP is an algorithm used to compute the marginalization of functions by passing messages on a graphical model. The algorithm was initially formalized for trees, and, in the case of trees, is known to solve the inference problem exactly. It is also empirically found to be working on many loopy graphs.
      BP is known to be well suited in several communication problems; e.g., decoding of turbo codes and LDPC codes, multiuser detection, signal detection in ISI channels. In this tutorial, we will illustrate that though the graphical models representing MIMO systems are fully/densely connected (loopy), some simple approximations, simplifications, and damping of messages in BP algorithms can lead to near-MAP performance particularly in large-MIMO settings.
    2.3. Probabilistic Data Association (PDA)
      PDA, originally developed for target tracking, is quite useful in digital
      communications. Particularly, PDA algorithm is a reduced complexity alternative to the a posteriori probability decoder/detector/equalizer. Near-optimal performance has been demonstrated for PDA-based multiuser detection in CDMA systems. In this tutorial, we will demonstrate near-MAP
      performance in large-MIMO detection using PDA.
    2.4. Markov Chain Monte Carlo (MCMC) Techniques
      MCMC methods, which are statistical methods based on Markov chain simulation, have been increasingly applied to solve detection/estimation problems in communication systems. In MCMC methods, statistical inferences are developed by simulating the underlying processes through Markov chains. By doing so, the exponential complexity of multi-dimensional systems can be reduced to a linear or at most a polynomial complexity. In this tutorial, we will show that certain randomizations to conventional MCMC methods can make MCMC algorihms achieve near-MAP performance in large-MIMO detection.
    3. Conclusions
    In summary, this tutorial will present a detailed exposition of various detection algorithms that our research group have identified and found to be suited for large-MIMO detection. We believe that several algorithms and variants other than the ones presented in the tutorial are possible, and that investigation of such algorithms for detection and channel estimation in the context of large-MIMO systems is a open and promising research topic. We also believe that this tutorial will motivate the participating researchers and engineers to consider and improve large-MIMO techniques and methods in their research and implementations.

Primary Audience
The target audience for this tutorial would include:
- Graduate students/faculty involved/interested in wireless PHY layer communications research.
- System designers of MIMO wireless communication systems.
- Industry engineers involved in implementation of MIMO receivers and MIMO-OFDM wireless systems.
- RF/Antenna engineers who would wish to get a system feasibility perspective of large-MIMO systems.
- Managers/engineers involved in wireless standardization efforts.

Till recently, MIMO systems with tens of antennas were thought to be impractical because of several technological challenges in implementing such large-MIMO systems. This tutorial presentation will illustrate that all the technological challenges can be overcome and that large-MIMO systems can be indeed implemented. Key developments that make this possible are 1) recently reported low-complexity large-MIMO detection algorithms from machine learning/artificial intelligence, and 2) maturity of compact antenna designs and RF technologies. Detection and channel estimation in large-MIMO systems is a promising, high-impact, emerging area.

A. Chockalingam received the B.E. (Honours) degree in Electronics and Communication Engineering from the P. S. G. College of Technology, Coimbatore, India, in 1984, the M.Tech. degree with specialization in satellite communications from the Indian Institute of Technology, Kharagpur, India, in 1985, and the Ph.D. degree in Electrical Communication Engineering (ECE) from the Indian Institute of Science (IISc), Bangalore, India, in 1993. During 1986 to 1993, he worked with the Transmission R & D division of the Indian Telephone Industries Limited, Bangalore. From December 1993 to May 1996, he was a Postdoctoral Fellow and an Assistant Project Scientist at the Department of Electrical and Computer Engineering, University of California, San Diego. From May 1996 to December 1998, he served Qualcomm, Inc., San Diego, CA, as a Staff Engineer/Manager in the systems engineering group. In December 1998, he joined the faculty of the Department of ECE, IISc, Bangalore, India, where he is a Professor, working in the area of wireless communications and networking. Recently, his research group (http://wrl.ece.iisc.ernet.in/) has been making pioneering contributions in the nascent field of low-complexity near-optimal detection in large-MIMO systems.

Dr. Chockalingam is a recipient of the Swarnajayanti Fellowship from the Department of Science and Technology, Government of India. He served as an Associate Editor of the IEEE Transactions on Vehicular Technology from May 2003 to April 2007. He currently serves as an Editor of the IEEE Transactions on Wireless Communications. He also served as a Guest Editor for the IEEE JSAC Special Issue on Multiuser Detection for Advanced Communication Systems and Networks. Currently, he serves as a Guest Editor for the IEEE JSTSP Special Issue on Soft Detection on Wireless Transmission. He is a Fellow of the Institution of Electronics and Telecommunication Engineers, and a Fellow of the Indian National Academy of Engineering.

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T6: Mobility models and social networks
Presented by: Paolo Santi, Istituto di Informatica e Telematica del CNR, Pisa, Italy
Time: 13:30–17:00
Room: Corso A


Mobility is a fundamental property of several short range wireless networks, such as vehicular networks, opportunistic networks, some types of sensor networks, and so on. Given this, significant efforts have been devoted in the wireless networking literature to deriving simple mobility models resembling salient features of these types of networks. As a result of these efforts, a plethora of mobility models have been introduced in the literature.
In this tutorial, we will give an organic view of this large body of literature, surveying the most representative mobility models introduced for general short-range wireless networks, as well as models tailored to more specific application scenarios such as vehicular and opportunistic networks. With respect to this latter type of network, we will carefully describe mobility models taking into account the social structure underlying opportunistic networks composed of mobile individuals (a.k.a. Pocket Switched Networks).
When presenting the mobility models, we will survey not only their definition and possible utilization, but also (when possible) their stationary properties for what concerns, e.g., node spatial distribution, average node velocity, etc. As we shall see, knowledge of stationary properties of a mobility model is fundamental in the set-up of an accurate mobile network simulation environment.

Tutorial Objectives
The tutorial will provide an exhaustive coverage of mobility models for short-range wireless networks. More specifically, the target will be on mostly infrastructure-less wireless networks based on existing or forthcoming technology (e.g., WiFi, Bluetooth, ZigBee, etc.), which will complement traditional infrastructured wireless networks (e.g., cellular) and make it possible the realization of the ubiquitous computing paradigm. Application scenarios for this type of wireless networks include WLAN(WirelessLocalAreaNetwork)/mesh networks, vehicular networks, and opportunistic networks. Since user mobility is a salient feature of next generation wireless networks, how to model user movement becomes a fundamental part of the wireless network performance evaluation process.
The goal of this tutorial is to give the reader an organic view of mobility modelling as a scientific discipline, encompassing both theoretical and practical aspects related to the challenging mobility modelling task.

Tutorial Outline

      Why mobility models for wireless multihop networks are so important?
      Why can't we simply models for cellular networks?
      A taxonomy
      "General purpose" mobility models
      Synthetic models: Random Walk, Random Waypoint, Group mobility
      Trace-based models for WLAN traces
    Mobility models for vehicular networks
      Macro-scopic and Micro-scopic models
      SUMO mobility model and TraNS
    Mobility models for opportunistic networks
      Routing in opportunistic networks
      Mobile social network analysis based on real-world traces
      Social-based mobility models: community-based Mobility Model, Small World in Motion model, Interest-based mobility models
    Conclusions and wrap-up

Primary Audience
The tutorial is aimed at PhD students, PostDocs, and more in general researchers and engineers working the in the wireless networking field.

Despite the importance of mobility modeling in wireless network performance analysis, no specific tutorial on mobility modeling has been presented in VTC conferences so far.

Dr. Santi received the Laura Degree and Ph.D. degree in computer science from the University of Pisa in 1994 and 2000, respectively. He is part of the research staff at the Istituto di Informatica e Telematica del CNR in Pisa, Italy, since 2001, first as a Researcher and now as a Senior Researcher.
During his career, he visited Georgia Institute of Technology in 2001, and Carnegie Mellon University in 2003. His research interests include fault-tolerant computing in multiprocessor systems (during PhD studies), and, more recently, the investigation of fundamental properties of wireless multihop networks such as connectivity, topology control, lifetime, capacity, mobility modeling, and cooperation issues. He has contributed more than 50 papers and a book in highly reputed conferences and journals in the field of wireless ad hoc, vehicular, and sensor networking.
Dr. Santi has been General Co-Chair of ACM VANET 2007 and 2008, Technical Program Co-Chair of IEEE WiMesh 2009, and he is involved in the organizational and technical program committee of several conferences in the field. Since February 2008, Dr. Santi is Associate Editor for IEEE Transactions on Mobile Computing. He is a member of IEEE CS, and a senior member of ACM and SIGMOBILE.

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