2017 Seminars

Fall 2017

Date: December 8, 2017

Time: 2:35 - 3:35. Lavery Hall, Room 330

Title: Wireless Communications with Unmanned Aerial Vehicles: Fundamentals, Deployment, and Optimization

Abstract: The use of aerial platforms such as unmanned aerial vehicles (UAVs) and drones is a promising solution for providing reliable and cost-effective wireless communications. In particular, UAVs can be quickly and efficiently deployed to support cellular networks and enhance their quality-of- service (QoS) by establishing line-of-sight (LoS) communication links. With their inherent attributes such as mobility, flexibility, and adaptive altitude, if properly deployed, UAVs admit several key potential applications in wireless systems. For instance, UAVs can be deployed to complement existing cellular systems by providing additional capacity to hotspot areas as well as to provide network coverage in emergency and public safety situations. Despite the several benefits and practical applications of using UAVs as aerial base stations, one must address many technical challenges such as three-dimensional (3D) deployment, performance analysis, mobility, air-to-ground channel modeling, user association, and flight time optimization.

This presentation will include an overview on the UAV-based communication systems, along with their key opportunities and challenges. Furthermore, two technical challenges in UAV-enabled wireless networks are investigated. In the first work, UAV communications under flight time considerations is studied. In particular, a novel framework for optimizing the performance of a UAV-based wireless system in terms of the amount of data transmitted to users as well as UAVs’ hover duration is proposed. In the second work, the efficient deployment and mobility of multiple UAVs used to collect data from ground Internet of Things (IoT) devices, is investigated.

Bio: Mohammad Mozaffari received his BSc in Electrical Engineering from Sharif University of Technology in Iran, and his MSc in Geomatics Engineering from University of Calgary, Canada. He is currently a PhD candidate at the Bradley Department of Electrical and Computer Engineering at Virginia Tech. His research interests include wireless communications and statistical signal processing with focus on unmanned aerial vehicle (UAV) communications, 5G networks, satellite communications and localization.


Date: November 10, 2017

Time: 2:35 - 3:35, Lavery Hall, Room 330

Title: Modeling and Analysis of Emerging Trends in Device-to-Device Networks

Abstract: Device-to-device (D2D) communications enabling direct communication between devices located in close proximity have several benefits compared to the conventional approach of communicating through a base station in a cellular network. First, the spectral efficiency of the direct link is typically much higher due to a smaller link distance. Second, this circumvents the need to establish an end-to-end link through a base station, thereby offloading traffic from cellular networks. Third, while the D2D network can be visualized as an ad hoc network, it incurs a much lower protocol overhead due to the assistance it gets from the existing cellular network. All these benefits make it an attractive component for both the current 4G and the future 5G networks. In this talk, I will present a new comprehensive framework for the analysis of D2D networks in which the device locations are modeled by a Poisson cluster process. This model accurately captures the fact that the devices engaging in D2D communication typically form small clusters. Using this model, we characterize the performance of a variety of device and cluster-centric content placement strategies. One of the key outcomes of our analysis is evaluating an optimum number of D2D transmitters that must be simultaneously activated per cluster to maximize area spectral efficiency.

Bio: Mehrnaz Afshang received her B.E. degree in Electrical Engineering from Shiraz University of Technology, Iran, in 2011 and her Ph.D. degree from Nanyang Technological University, Singapore, in 2016. During her Ph.D., she was a recipient of the SINGA Fellowship. Since January 2015, she has been a visiting student and later a postdoctoral associate in the Bradley Department of Electrical and Computer Engineering at Virginia Tech working with Dr. Dhillon. Her research interests include communications theory, stochastic geometry, device-to-device networks, and wireless ad hoc and heterogeneous cellular networks. She was selected as one of the 60 world’s brightest women to participate in the Rising Stars Workshop in 2016.


Date: November 3, 2017

Time: 2:45 - 3:45

Title: Parametric Channel Estimation for 3D mmWave Massive MIMO/FD-MIMO Systems

Abstract: In order to meet the challenge of increasing data-rate demand as well as the form factor limitation at the base station (BS), 3D massive multiple-input multiple-output (MIMO)/full dimensional(FD) MIMO has been introduced as one of the enabling technologies for fifth-generation mobile cellular systems. In 3D massive MIMO systems, especially in TDD mode of operation, a BS will rely on the uplink sounding signals from mobile stations to obtain the spatial information for downlink MIMO operations. Accordingly, multi-dimensional parameter estimation for the massive MIMO channel becomes crucial for such systems to realize the predicted capacity gains.

In this talk, we will be presenting a channel estimation framework for 3D massive MIMO/FD-MIMO systems under parametric channel modeling. We will first introduce a separate low-complexity parameter estimation algorithm based on unitary transformation. We will then present some analytical characterizations for the channel estimation performance in terms of mean squared error (MSE), and highlight on the key system-level intuitions we can have from these analytical results. We will demonstrate how a matrix-based joint parameter estimation can achieve superior performance than the separate estimation method. Finally, we will conclude the presentation by showing how the channel parameters estimated in the uplink can be utilized in optimum downlink precoder design.

Bio: Rubayet Shafin received his BS degree in Electrical and Electronics Engineering from Bangladesh University of Engineering and Technology in 2013, and MS degree in Electrical Engineering from University of Kansas (KU) in 2017. From 2014 to 2017, he was affiliated with Information and Telecommunication Technology Center (ITTC) at KU, where he worked on wireless channel estimation, precoder design, and performance characterization for 3D mmWave and massive MIMO systems. He spent his summer 2017 as a research intern at Huawei R&D, USA, where he worked on the development of a novel channel estimation framework for 5G system. His present research interests include different physical layer aspects of 5G network and application of machine learning in wireless communication. He is currently a PhD student in the ECE department at Virginia Tech, and is advised by Dr. Lingjia Liu.


Date: October 27, 2017

Time: 2:35pm-3:35pm

Title: Integrated mmWave Access and Backhaul in 5G: Bandwidth Partitioning and Downlink Analysis

Abstract: With the increasing network densification, it has become exceedingly difficult to provide traditional fiber backhaul access to each cell site, which is especially true for small cell base stations (SBSs). The increasing maturity of millimeter wave (mmWave) communication has opened up the possibility of providing high-speed wireless backhaul to such cell sites. Since mmWave is also suitable for access links, the third generation partnership project (3GPP) is envisioning an integrated access and backhaul (IAB) architecture for the fifth generation (5G) cellular networks in which the same infrastructure and spectral resources will be used for both access and backhaul.

In this talk, I will be presenting our recently developed analytical framework for IAB-enabled cellular network using which we can accurately characterize its downlink rate coverage probability. For this model, we study the performance of two backhaul bandwidth (BW) partition strategies, (i) equal partition: when all SBSs obtain equal share of the backhaul BW, and (ii) load-based partition: when the backhaul BW share of an SBS is proportional to its load. Our analysis shows that depending on the choice of the partition strategy, there exists an optimal split of access and backhaul BW for which the rate coverage is maximized. Further, we have found that there exists a critical volume of cell-load (total number of users) beyond which the gains provided by the IAB-enabled network disappear and its performance converges to that of the traditional macro-only network with no SBSs. We will conclude the talk by demonstrating our initial results on the impact of different cell association strategies using sub-6 GHz and mmWave signalling on the rate coverage probability.

Interested readers can refer to https://arxiv.org/abs/1710.06255 for more rigorous details.

Bio: Chiranjib Saha received the Bachelor in Engineering degree in electronics and telecommunication engineering from Jadavpur University, India, in 2015. He is a third year Ph.D. student in the Bradley Department of Electrical and Computer Engineering, Virginia Tech, advised by Dr. Harpreet S. Dhillon. He was the recipient of the Wireless Fellowship Award by Wireless@VT, Virginia Tech, in 2015. His research interests have been broadly focused on modelling and analysis of heterogeneous cellular networks using the tools of stochastic geometry and the theory of point processes. He is currently working on the backhaul design aspects of 5G HetNets using mmWave communication.


Date: October 6, 2017

Time: 3 pm. - 3:40 p.m., at Lavery Hall,, Room 330

Title: Spectrum Sharing Test & Demonstration - LTE Field Measurement

Abstract: As part of the DoD Transition Plan for the Advanced Wireless Services (AWS) -3, the Defense Information Systems Agency (DISA) Defense Spectrum Organization (DSO) has sponsored the Spectrum Sharing Test & Demonstration (SST&D) program. The current objectives of the SST&D Program include:

  • Facilitating expedited and expanded entry of commercial wireless network deployments into the 1755-1780 MHz band.
  • Identify, demonstrate, and operationalize interference mitigation techniques consistent with commercial Long Term Evolution (LTE) standards that support increased sharing between LTE and incumbent DoD systems
  • Prototype and demonstrate advanced sharing concepts for long-term sharing in the 1755-1780 MHz band.
     

The SST&D LTE Characterization activity is focused on understanding LTE uplink emissions under a variety of operating conditions (e.g., traffic loading; ISD; power control algorithms; resource allocation algorithms; etc.) and in the presence of interfering signals (e.g. DoD SATOPS). As part of the LTE Characterization activity, the team is deploying LTE Field Measurement Systems to perform LTE network testing and measurements providing real network operation statistics to inform our LTE network modeling efforts and enhance spectrum coexistence assessments.

The LTE Field Measurement System provides a means to collect and record sector DL and UL control signaling and simultaneous UL RF emissions. This measurement system paired with our developed field measurement data analysis and reporting capability provides the ability to generate network UL parameter statistics from real LTE networks.

Bio: Mr. Mike Smith is a Senior Systems Engineer for the Virginia Tech Applied Research Corporation (VT-ARC). Mr. Smith is an experienced Systems Engineer with functional expertise in Wireless Networking, Communication Systems, RF Design, Semiconductor Manufacturing, and Satellite Communications. He has demonstrated experience in planning, analysis, design, and delivery of communications systems for the DoD and Commercial Service Providers. Prior to joining VT-ARC, Mr. Smith was a Systems Engineer at Harris Corporation Electronic Systems focused on developing and managing Software Defined Tactical Radios and Power Amplifiers for DoD and foreign military customers. Earlier in his 22-year Harris career, Mike focused on business development for Harris commercial satellite systems, serving as Lead Systems Engineer on several payload programs and proposals emphasizing the application of phased array antenna systems. Earlier in his career, Mr. Smith also successfully led the development and implementation of semiconductor manufacturing yield management methodologies at Filtronic Solid State and Zeevo, Inc. Mr. Smith received a B.S. degree in Electrical Engineering from Virginia Tech in 1985.


Date: October 6, 2017

Time: 4:00 pm. - 5 pm. Location: Goodwin Hall, Room 190

Title: Dependability for Computer Systems meets Data Analytics

Abstract: We live in a data-driven world as everyone around has been telling us of late. Everything is generating data, sometimes volumes of it, from the sensors embedded in our physical spaces to the large number of machines in data centers which are being monitored for a wide variety of metrics. The question that we pose is: Can the volume of data be used for improving the dependability of computing systems?

Dependability is simply the property that the system continues to provide its functionality despite the introduction of faults, either accidental faults (design defects, environmental effects, etc.) or maliciously introduced faults (security attacks, either external or internal). The computing systems that we target have been increasing in scale, both in terms of the number of executing elements and the amount of data that they need to process. For example, a large number of data-spewing sensors on mobile and embedded devices coupled with the large number of such devices show such increases in scale. We have been addressing the dependability challenge through large-scale data analytics in three broad domains: embedded and mobile networks, scientific computing clusters and applications, and computational genomics. In this talk, I will first give a high-level view of the dependability challenges in these three domains, how data analytics has been brought to bear on these challenges, and some of our key results. I will then go into two recent developments: dependability in a cellular network and dependability through approximating computation. In the first development, we answer the question – can the cellular network and the smart mobile devices working together mitigate the problem of network outages or reduced data bandwidth. In the second development, we answer the question – can the limitations of human perception be leveraged to approximate certain computation and thus allow the computation to meet timing guarantees, even when executing on resource-constrained platforms. A common example is video processing where the human visual system is forgiving for certain kinds of inaccuracy. I will conclude with some insights about how the power of data analytics can help us create more dependable systems.

Bio: Saurabh Bagchi is a Professor in the School of Electrical and Computer Engineering and the Department of Computer Science at Purdue University in West Lafayette, Indiana. He is the founding Director of a university-wide resiliency center at Purdue called CRISP (2017-present). He is an ACM Distinguished Scientist (2013), a Senior Member of IEEE (2007) and of ACM (2009), a Distinguished Speaker for ACM (2012), and an IMPACT Faculty Fellow at Purdue. He is the recipient of an IBM Faculty Award (2014), a Google Faculty Award (2015), and the AT&T Labs VURI Award (2016). He was elected to the IEEE Computer Society Board of Governors for the 2017-19 term.

Saurabh's research interest is in distributed systems and dependable computing. He is proudest of the 18 Ph.D. students who have graduated from his research group and are in various stages of building wonderful careers in industry or academia. In his group, he and his students have far too much fun building and breaking real systems. Saurabh received his MS and Ph.D. degrees from the University of Illinois, Urbana-Champaign and his BS degree from the Indian Institute of Technology Kharagpur, all in Computer Science.


Date: September 29, 2017

Title: Adaptive Pilot Patterns for CA-OFDM Systems in Vehicular Channels

Abstract: 5G is expected to bring vast performance improvements in various aspects, particularly in user data rates. At the PHY layer, enhancements in spectral efficiency are achieved by a combination of technologies, such as massive MIMO, carrier aggregation, higher-order modulation and spectrally efficient multicarrier waveforms. Minimizing the system overhead is one of the critical challenges that standardization bodies are facing to meet the performance criteria promised for 5G. In this regard, pilot overhead is a major design issue at the PHY layer. Pilots (or reference signals) are necessary for mobile communication in dynamic environments and, therefore, cannot be eliminated. They enable accurate estimation of the channel state information (CSI) and hence, are necessary to truly realize the performance gains promised by these technologies. In this talk, we present our method to maximize throughput using pilot pattern adaptation in a MIMO-OFDM point-to-point link. Our algorithm is based on adapting the pilot density and power as a function of the channel fading characteristics using feedback of indices from a 'channel-statistics codebook' known to the transmitter and receiver. We demonstrate the throughput gains of our scheme when compared to LTE, in terrestrial and aerial vehicular channels. We extend this scheme to carrier aggregation (CA)-OFDM systems, and leverage knowledge about the frequency dependence of the channel statistics to minimize the feedback requirements. We conclude with a discussion on important practical considerations to realize adaptive pilot patterns in future wireless networks. This work was carried out under the supervision of Dr. Vuk Marojevic and Dr. Jeffrey H. Reed and has been accepted in a future issue of the IEEE Transactions on Vehicular Technology. Interested readers are encouraged to read the prepub versions on

arXiv: https://arxiv.org/pdf/1709.03176.pdf

IEEE Xplore: http://ieeexplore.ieee.org/document/8031997/ (DOI: 10.1109/TVT.2017.2751548)

Bio: Raghunandan M. Rao received the B.Eng. degree in Telecommunication engineering from R V College of Engineering, Bangalore, India, in 2011, the M.Tech. degree in Laser Technology from the Indian Institute of Technology Kanpur, India in 2013, and the M.S. degree in Electrical Engineering from Virginia Tech (VT), Blacksburg, VA, USA, in 2016. He is currently working towards the Ph.D. degree in the Bradley Department of ECE, VT and is affiliated with the Wireless@VT research group. In the past he has worked as a summer intern at Blue Danube Systems Inc, Santa Clara, CA, USA. His research interests include LTE and 5G NR, mmWave wireless communications, massive MIMO, and spectrum sharing.


Date: September 15, 2017

Title: 3GPP-inspired Stochastic Geometry Models for Cellular Networks

Abstract: The growing complexity of heterogeneous cellular networks (HetNets) has necessitated a variety of user and base station (BS) configurations to be considered for realistic performance evaluation and system design. This is directly reflected in the HetNet simulation models used by the standardization bodies, such as the third generation partnership project (3GPP). Complementary to these simulation models, stochastic geometry-based approach, modeling the locations of the users and the K tiers of BSs as independent and homogeneous Poisson point processes (PPPs), has gained prominence in the past few years. Despite its success in revealing useful insights, this PPP-based model is not rich enough to capture all the spatial configurations that appear in real world HetNet deployments (on which 3GPP simulation models are based).

In this talk, we will demonstrate that modeling a fraction of users and some BS tiers alternatively with a Poisson cluster process (PCP) captures the aforementioned coupling, thus bridging the gap between the 3GPP simulation models and the PPP-based analytic model for HetNets. For this model, we will show that the downlink coverage probability of a typical user under maximum signal-to-interference-ratio association can be expressed in terms of the sum-product functionals over PPP, PCP, and its associated offspring point process, which are all characterized as a part of our analysis. Special instances of the proposed model will be shown to closely resemble different configurations considered in 3GPP HetNet models. Our analysis concretely demonstrates that the performance trends are highly sensitive to the assumptions made on the user and BS configurations. We will conclude the talk by elaborating on this by going into the scaling laws for such models, which will also reveal useful performance trends in the "ultra-dense" HetNet setting.

This is joint work with Chiranjib Saha and Mehrnaz Afshang. Interested readers can refer to https://arxiv.org/abs/1705.01699https://arxiv.org/abs/1612.07285, and https://arxiv.org/abs/1606.06223 for more rigorous details.

Bio: Harpreet S. Dhillon received the B.Tech. degree in Electronics and Communication Engineering from IIT Guwahati in 2008, the M.S. degree in Electrical Engineering from Virginia Tech in 2010, and the Ph.D. degree in Electrical Engineering from the University of Texas at Austin in 2013. In academic year 2013-14, he was a Viterbi Postdoctoral Fellow at the University of Southern California. He joined Virginia Tech in August 2014, where he is currently an Assistant Professor of Electrical and Computer Engineering. He has also held short-term visiting positions at Alcatel-Lucent Bell Labs, Samsung Research America, and Qualcomm. His research interests include communication theory, stochastic geometry, and wireless ad hoc and heterogeneous cellular networks.

He is a recipient of five best paper awards including the 2016 IEEE Communications Society (ComSoc) Heinrich Hertz Award, the 2015 IEEE ComSoc Young Author Best Paper Award, the 2014 IEEE ComSoc Leonard G. Abraham Prize, and conference best paper awards at IEEE ICC 2013 and European Wireless 2014. His other academic honors include the 2017 Outstanding New Assistant Professor Award from the Virginia Tech College of Engineering, the 2013 UT Austin WNCG leadership award, the UT Austin MCD Fellowship, and the 2008 Agilent Engineering and Technology Award. He currently serves as an Editor for the IEEE Transactions on Wireless Communications, the IEEE Transactions on Green Communications and Networking, and the IEEE Wireless Communications Letters.


Spring 2017

Date: Friday, April 21, 2017

Title: Stochastic Geometry-based Modeling and Analysis of Citizens Broadband Radio Service System

Abstract: In April 2015, FCC approved (along with certain guidelines) the co-existence of the commercial networks alongside the incumbent systems in the underutilized 3.5 GHz band, a.k.a citizens broadband radio service (CBRS) band. In this talk, we will discuss the co-existence performance between a licensed and an unlicensed operator in this band. The focus of this talk will be on the successful application of tools from stochastic geometry to model and analyze the above system adhering to the key guidelines from the FCC such as protection zones around each licensed BS where the unlicensed BSs operation is prohibited and the use of contention-based channel access mechanism (CSMA-CA) among the unlicensed BSs. To this end, we will explore a couple of interesting point processes such as Poisson hole process and Matern hard-core process that are helpful in accurate modeling of the above system. We will also discuss the effect of different system parameters, such as protection zone radius, carrier sense threshold on the performance of both the operators in terms of their coverage probabilities and area spectral efficiencies.

Bio: Priyabrata Parida is a Ph.D. student in the Department of ECE at Virginia Tech. He is advised by Dr. Harpreet S. Dhillon. He received his bachelor’s degree in Electronics and Communications Engineering from the National Institute of Technology, Durgapur, India, in 2010, and his master’s degree in Telecommunications from the IIT Kharagpur, India, in 2015. His research interest includes modeling and analysis of fifth-generation cellular networks using tools from stochastic geometry, multi-antenna communication systems, and resource allocation in wireless systems. He has held summer internship position at MediaTek Inc., San Jose, USA, during 2016. In the past, he has worked at Idea cellular, a cellular operator in India, as an Assistant Manager, and at IIT Kharagpur as a Research consultant.


Date: Friday, April 7, 2017

Title: Joint Uplink and Downlink Coverage Analysis of Cellular-based RF-powered IoT Network

Abstract: Ambient radio frequency (RF) energy harvesting has emerged as a promising solution for powering small devices and sensors in massive Internet of Things (IoT) ecosystem due to its ubiquity and cost efficiency. In this presentation, a stochastic geometry-based analysis of the joint uplink and downlink coverage of cellular-based ambient RF energy harvesting IoT will be discussed. In the considered system, the cellular network is assumed to be the only source of RF energy. A time division-based approach is assumed for power and information transmission where each time slot is partitioned into three sub-slots: (i) charging sub-slot during which the cellular base stations (BSs) act as RF chargers for the IoT devices, which then use the energy harvested in this sub-slot for information transmission and/or reception during the following sub-slots, (ii) downlink sub-slot during which the IoT device receives information from the associated BS, and (iii) uplink sub-slot during which the IoT device transmits information to the associated BS. For this setup, the key technical challenges in deriving the joint uplink and downlink coverage probability are discussed.

Bio: Mustafa Kishk is a Ph.D. student in the Bradley Department of Electrical and Computer Engineering at Virginia Tech under the supervision of Prof. Harpreet Dhillon. He received his B.Sc. and M.S. degrees in Electronics and Electrical Communications Engineering from Cairo University, Egypt, in 2013 and 2015, respectively. His research interests include stochastic geometry, energy harvesting communication networks, cognitive radio, physical layer security, and multi-user communications.


Date: Friday, March 31, 2017

Title: Robust Communications with Paramorphic Multicarrier Waveforms

Abstract: A method for constructing robust multicarrier waveforms through cyclostationary properties is proposed. Spectral redundancy is created through symbol repetition in both time and frequency, and the optimal filter is presented which combines the redundancies. The method is compared against other filtering techniques and error correcting codes. A background on cyclostationarity and Frequency Shift (FRESH) filtering is given to provide context.

Bio: Matt Carrick is a Ph.D. student studying under Dr. Jeff Reed. He received his BSEE from George Mason University in 2007 and his MSEE from Virginia Tech in 2009. He is interested in cyclostationarity, digital signal processing and multirate signal processing.


Date: Friday, March 3, 2017

Location: Room 155, Goodwin Hall

Time: 2:35 p.m.

Title: Coexistence of Dedicated Short Range Communications (DSRC) and Wi-Fi: Implications to Wi-Fi Performance

Abstract: The 5.9 GHz band is being actively explored for possible spectrum sharing opportunities between Dedicated Short Range Communications (DSRC) and IEEE 802.11ac networks in order to address the increasing demand for bandwidth-intensive Wi-Fi applications. This talk discusses our study on the implications of this spectrum sharing to the performance of Wi-Fi systems. Through experiments performed on our testbed, we first investigate band sharing options available for Wi-Fi devices. Using experimental results, we show the need for using conservative Wi-Fi transmission parameters to enable harmonious coexistence between DSRC and Wi-Fi. Moreover, we show that under the current 802.11ac standard, certain channelization options, particularly the high bandwidth ones, cannot be used by Wi-Fi devices without causing interference to the DSRC nodes. Under these constraints, we propose a Real-time Channelization Algorithm for Wi-Fi Access Points operating in the shared spectrum.

Bio: Gaurang Naik is currently a Ph.D. candidate in Electrical Engineering at the Advanced Research in Information Assurance and Security (ARIAS) Lab at Virginia Tech. He is advised by Dr. Jung-Min (Jerry) Park, and his research focuses on problems related to dynamic spectrum sharing. He received his B.E. degree from the University of Mumbai, India, in 2012, and his M. Tech. degree from the Indian Institute of Technology Bombay in 2015.


Date: Friday, February 24, 2017

Speaker: Sundar Aditya (visiting Ph.D. student, University of Southern California)

Time: 2:35 p.m.

Location: Goodwin Hall, Room 155

Title: Bayesian Multi-Target Localization under environment-induced correlated blocking

Abstract: The canonical localization problem involves determining the position of one or more targets in an environment by analyzing wireless signals emanating from them at known receiver/anchor locations. This talk addresses the problem of localizing an unknown number of passive (i.e., reflecting) targets, all having the same radar signature, by a distributed radar consisting of single antenna transmitters and receivers that cannot determine directions of departure and arrival. Furthermore, the presence of multipath propagation and the possible (statistically dependent) blocking of the direct paths (DPs) are also considered. In its most general form, this problem can be cast as a Bayesian estimation problem where every multipath component is accounted for. However, when the environment map is unknown, this problem is ill-posed and hence, a tractable approximation is derived where only DPs are accounted for. In particular, we take into account the dependent blocking by distributed (i.e., non-point) obstacles in the environment which appears as a prior term in the Bayesian estimation framework. A sub-optimal polynomial-time algorithm to solve the Bayesian multi-target localization problem with dependent blocking is proposed and results show that when the DP blocking events are highly dependent, assuming them to be independent and having constant probability (as was done previously) resulted in poor detection performance, with false-alarms more likely to occur than detections.

Bio: Sundar Aditya obtained his B. Tech and M. Tech degrees in electrical engineering from the Indian Institute of Technology, Madras, in 2011. He is currently a Ph.D. candidate at the Wireless Devices and Systems (WiDeS) lab at the University of Southern California (USC), where his research focuses on problems pertaining to multi-target localization and tracking as well as the design and performance analysis of localization networks using stochastic geometry. He is jointly advised by Prof. Andreas F. Molisch from USC and Prof. Harpreet S. Dhillon from Virginia Tech.


Date: January 27, 2017

Speaker: Prof. Georgios B. Giannakis, ADC Chair in Wireless Telecommunications and McKnight Presidential Chair in ECE, University of Minnesota

Title: Inference and Learning over Large-Scale Social Networks

Abstract: Social networks are pervasive and encompass interactions over online social media, human links in epidemic processes, terrorist cells, and collaborations among researchers. The value in understanding and predicting complex network behavior cannot be understated, thanks to the growing role of search engines, cyber warfare, online marketing, and social recommendation tools. Real-world social networks are fraught with unique challenges that limit the efficacy of contemporary tools. For example, such networks are big (billions of nodes), evolve over time, and are often not directly observable. Viewed through a statistical learning lens, many network analytics problems boil down to (non-) parametric regression and classification, dimensionality reduction, or clustering. Adopting this point of view, this talk will put forth novel learning approaches for network visualization, anomaly and community detection, prediction of network processes, and dynamic network inference. Key emphasis will be placed on parsimonious models exploiting sparsity, low rank, or low-dimensional manifolds, attributes that have been shown useful for complexity reduction. The merits of the novel schemes will be demonstrated on both simulated and real-world social networks.

Bio: Georgios B. Giannakis (Fellow’97) received his Diploma in Electrical Engr. from the Ntl. Tech. Univ. of Athens, Greece, 1981. From 1982 to 1986 he was with the Univ. of Southern California (USC), where he received his MSc. in Electrical Engineering, 1983, MSc. in Mathematics, 1986, and Ph.D. in Electrical Engr., 1986. He was with the University of Virginia from 1987 to 1998, and since 1999 he has been a professor with the Univ. of Minnesota, where he holds a Chair in Wireless Telecommunications, a University of Minnesota McKnight Presidential Chair in ECE, and serves as director of the Digital Technology Center. His general interests span the areas of communications, networking, and statistical signal processing – subjects on which he has published more than 400 journal papers, 680 conference papers, 25 book chapters, two edited books and two research monographs (h-index 119). Current research focuses on big data analytics, wireless cognitive radios, network science with applications to social, brain, and power networks with renewables. He is the (co-) inventor of 25 patents issued, and the (co-) recipient of 8 best paper awards from the IEEE Signal Processing (SP) and Communications Societies, including the G. Marconi Prize Paper Award in Wireless Communications. He also received Technical Achievement Awards from the SP Society (2000), from EURASIP (2005), a Young Faculty Teaching Award, the G. W. Taylor Award for Distinguished Research from the University of Minnesota, and the IEEE Fourier Technical Field Award (2015). He is a Fellow of EURASIP, and has served the IEEE in a number of posts including that of a Distinguished Lecturer for the IEEE-SP Society.