2018 Seminars

Fall 2018

Date: November 29, 2018

Speaker:  Aritra Konar, University of Virginia

Title: Submodular and Stochastic Optimization for Transmit Antenna Selection and Beamforming in 5G

Abstract: Many interesting optimization problems in wireless communications are computationally hard. Optimal solutions are often intractable, and even good approximations are too demanding for actual implementation. Very recently, we have come to realize that staple tools in optimization theory, such as stochastic gradient descent and greedy approximation, can be brought to bear on hard communications problems, with surprising success. In this talk, I will take on two examples of NP-hard communication problems, namely, transmit antenna selection in multi-user MIMO, and minimum outage beamforming. For the former, we will see how a simple greedy algorithm can be used to guarantee a high-quality approximate solution. For the latter, I will explain how very simple but judicious stochastic approximation can outperform much more sophisticated and complex algorithms. 

Bio: Aritra Konar received the B.Tech. degree in Electronics and Communications Engineering from West Bengal University of Technology, West Bengal, India, and the M.S. and Ph.D. degrees in Electrical Engineering from the University of Minnesota, Minneapolis, USA, in 2011, 2014, and 2017 respectively. He is currently a Postdoctoral Associate in the Department of ECE, University of Virginia, VA, USA. His research interests include statistical signal processing, wireless communications, nonlinear optimization and data analytics. 


Date: November 9, 2018

Speaker:  Jaya Kartheek Devineni, Wireless@VT

Title: Ambient Backscatter Systems: Exact Average Bit Error Rate Under Fading Channels

Abstract: The success of Internet-of-Things (IoT) paradigm relies on, among other things, developing energy-efficient communication techniques that can enable information exchange among billions of battery-operated IoT devices. With its technological capability of simultaneous information and energy transfer, ambient backscatter is quickly emerging as an appealing solution for this communication paradigm, especially for the links with low data rate requirement. In this presentation, we study signal detection and characterize exact bit error rate for the ambient backscatter system. In particular, we formulate a binary hypothesis testing problem at the receiver and analyze system performance under three detection techniques: a) mean threshold (MT), b) maximum likelihood threshold (MLT), and c) approximate MLT. Motivated by the energy-constrained nature of IoT devices, we perform the above analyses for two receiver types: i) the ones that can accurately track channel state information (CSI), and ii) the ones that cannot. Two main features of the analysis that distinguish this work from the prior art are the characterization of the exact conditional density functions of the average received signal energy, and the characterization of exact average bit error rate (BER) for this setup. The key challenge lies in the handling of correlation between channel gains of two hypotheses for the derivation of joint probability distribution of magnitude squared channel gains that is needed for the BER analysis.

Bio: Jaya Kartheek Devineni received the B.Tech. degree in Electronics and Communication Engineering from the Indian Institute of Technology (IIT) Guwahati, India in 2013. He is currently pursuing the Ph.D. degree in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. His research interests include design and analysis of ambient backscatter systems, and analysis of wireless networks using stochastic geometry. During his Bachelor's degree, he interned at Research Center Imarat (RCI) Hyderabad, India and Indian Institute of Science (IISc) Bangalore, India. After his Bachelor's graduation, he worked as an ASIC verification engineer in Bangalore, India.


Date: November 2, 2018

Speaker: AbdelRahman Eldosouky, Wireless@VT

Title: Resilient Critical Infrastructure: Bayesian Network Analysis and Contract-Based Optimization

Abstract: Critical infrastructure (CI), such as power grids and transportation systems, are vital to modern day cities and communities. As such, maintaining proper operation of CIs, in presence of failures or security threats, is therefore a critical challenge. In particular, resilience is a key measure that can be used to evaluate the ability of an infrastructure to deliver its designated service, under potentially disruptive situations. Resilience, in literature, has multiple definitions that are typically application dependent, however, most of these definitions pertain to resilience in response to a change in or a corruption to the system’s normal functionality. Given that CIs cut across multiple domains and that the resilience lacks a standard definition, resilience improvement techniques are typically infrastructure-specific.

In this talk, the problem of optimizing and managing the resilience of CIs using our comprehensive two-fold framework, is presented. Within the framework, a novel analytical resilience index is proposed to measure the effect of each CI’s physical components on its probability of failure. In particular, a Markov chain defining each CI’s performance state and a Bayesian network modeling the probability of failure are introduced to infer each CI’s resilience index. Then, to maximize the resilience of a system of CIs, a novel approach for allocating resources, such as drones or maintenance personnel, is proposed. In particular, a comprehensive resource allocation framework, based on the tools of contract theory, is proposed enabling the system operator to optimally allocate resources, such as, redundant components or monitoring devices to each individual CI based on its economic contribution to the entire system. The proposed framework is evaluated using a case study pertaining to hydropower dams and their interdependence to the power grid.

Bio: AbdelRahman Eldosouky received his B.Sc in Computer and Control Engineering from Zagazig university in Egypt, and his M.Sc in Wireless Networks from the same university. He is currently a PhD candidate at the Bradley Department of Electrical and Computer Engineering at Virginia Tech, under the supervision of Dr. Walid Saad. In summer 2015, he attended a graduate summer school at the institute of pure & applied mathematics (IPAM) at UCLA titled "Games and Contracts for Cyber-Physical Security". His research interests include Critical Infrastructure Resilience, Cyber-Physical Security, Moving Target Defense, and the Internet of Things (IoT).


Date: September 14, 2018

Speaker: Dr. Yaling Yang, Wireless@VT

Title: Understanding the Threat of GPS Spoofing Attack to Everyday Applications

Abstract: GPS is a critical wireless infrastructure that provides global localization and timing services. However, due to the lack of inherent security measures in civilian GPS,  it is easy to spoof GPS signals.

In this talk, we will first overview the threat of GPS spoofing to some of the critical applications and then explore the feasibility of launching GPS spoofing attack on car navigation systems.  Finally, we will  discuss some of the state-of-art countermeasures to GPS spoofing attack.

Bio: Yaling Yang is currently an associate professor in the Electrical and Computer Engineering department of Virginia Tech. She received her doctorate in computer science in the summer of 2006 from the University of Illinois at Urbana-Champaign. She has concentrated her research on design, modeling and analysis of networking systems and security systems.  She has been named the faculty fellow of Virginia Tech's college of engineering in 2016 and is an NSF Faculty Early Career Award winner.  She has been the principle investigator of eight NSF funded projects.  For more information about Dr. Yang, please view her ECE biography page.


Spring 2018

Date: March 30, 2018

Speaker: Dr. Sudeep Bhattarai, Wireless@VT

Time: 2:40 - 3:40. Lavery Hall, Room 330

Title: Performance Analysis of IEEE 802.11ax in a Heterogeneous Wi-Fi Network

Abstract: Legacy Wi-Fi technologies are inept at coping with the traffic demands of users in a congested environment because their medium access technique, which is common to all legacy technologies, is suboptimal in terms of spectrum utilization efficiency. To address this limitation, the next generation Wi-Fi technology-referred to as IEEE 802.11ax-introduces some key features, most notably the use of Multi-User Orthogonal Frequency Division Multiple Access (MU-OFDMA) technology in its medium access control (MAC) layer. MU-OFDMA allows multiple users to transmit simultaneously in smaller sub-channels (a.k.a. resource units (RUs)) in the uplink (UL) as well as the downlink (DL), thereby improving the 802.11ax MAC efficiency, especially when the network size is large. Furthermore, for UL MU-OFDMA transmissions, in order to provide a flexible support to all stations (STAs) in the network, 802.11ax provisions Random Access (RA) RUs as well as Scheduled Access (SA) RUs. In this talk, we first present our analysis on the MAC layer performance of the new MU OFDMA-based 802.11ax and summarize our findings. Second, we discuss the impact of different RA RU and SA RU distributions on the network performance and devise an algorithm for optimal RU allocation such that the overall 802.11ax throughput is maximized. Third, we study how to share the airtime between legacy 802.11 and 802.11ax transmissions fairly when both categories of STAs are jointly served by a single access point (AP) that can support both 802.11ax and legacy Wi-Fi. Finally, we present our results, based on our implementation of the proposed 802.11ax MAC on network simulator-3 (NS-3), and validate our analysis by comparing theoretical results with those obtained from extensive NS-3 simulations.

Bio: Sudeep Bhattarai received his Bachelor’s degree in electronics and communication engineering from Tribhuvan University, Nepal, and his Master’s degree in electrical engineering from Tennessee State University, USA, in 2011 and 2013, respectively. He recently defended his Ph.D. in Electrical Engineering at Virginia Tech. Sudeep was a research intern at AT&T Labs and Google in summer 2014 and summer 2016 respectively. Sudeep is a recipient of the best paper award at IEEE DySPAN 2014. His current research interests include dynamic spectrum sharing, coexistence among heterogeneous wireless technologies, next generation wireless networks, and privacy issues in wireless communications.


Date: March 23, 2018

Speaker: Vishnu Vardhan Chetlur, Wireless@VT

Time: 2:40 - 3:40. Lavery Hall, Room 330

Title: Modeling and Analysis of Vehicular Communication Networks: A Stochastic Geometry approach

Abstract: Vehicular communication, which collectively refers to vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, has enabled the vehicular nodes to share information with each other and also with roadside units (RSUs) to improve the road safety and transport efficiency. With autonomous vehicles becoming a reality in the near future, the data traffic originating from vehicular networks is expected to increase many folds while also putting more stringent latency and connectivity constraints compared to the networks of today. In the latest 3GPP release, the support for vehicle-to-everything (V2X) communication has also been added to the long term evolution (LTE). Vehicular networks have a peculiar spatial geometry as the locations of nodes is restricted to the roadways. In this talk, we will discuss a doubly stochastic spatial model that can capture this coupling between the nodes and the underlying infrastructure. We will then analyze the signal-to-interference ratio (SIR) based coverage of a typical vehicular user in the network. The analysis reveals some interesting trends in the coverage as a function of density of nodes and also the layout of roads. These results also underline the importance of the doubly stochastic model for the analysis of vehicular networks.

Bio: Vishnu Vardhan Chetlur received the B.E. degree (Hons.) in electronics and communications engineering from the Birla Institute of Technology and Science (BITS), Pilani, India, in 2013. He graduated top of his class in the Department of Electrical Engineering at BITS, and received the institute Silver Medal for being ranked second in the whole institute. He was a recipient of the BITS Merit Scholarship for his excellence in academics. After his bachelor's graduation, he was a Design Engineer with Redpine Signals Inc., for two years. He is currently pursuing the Ph.D. degree with Virginia Tech under the supervision of Dr. Harpreet S. Dhillon. His research interests include wireless communication, vehicular networks, stochastic geometry and smart cities.


Date: February 23, 2018

Speaker: Dr. Mahi Abdelbar, Wireless@VT

Time: 2:40 - 3:40. Lavery Hall, Room 330

Title: Pedestrian GraphSLAM using Smartphone-based PDR in Indoor Environments

Abstract: Simultaneous Localization and Mapping (SLAM) for pedestrians is a relatively new approach for the indoor localization problem. Adopted from robotics, SLAM for indoor pedestrians presents a new and efficient framework for tracking users’ movement trajectories within buildings. With the advancements in smartphone technology, pedestrian SLAM has transitioned towards utilizing smartphones’ integrated sensors through Pedestrian Dead-Reckoning (PDR) techniques. GraphSLAM models the spatial structure of a sequence of user’s positions inside a building as a graph optimization problem, based on estimated positions through PDR. In addition, GraphSLAM depends on loop-closures and/or landmarks as constraints in the trajectory estimation problem, which in pedestrian SLAM is still very challenging.

In this talk, we first present an overview of the pedestrian SLAM problem and its challenges, as a solution to the indoor localization problem. This work comes at the intersection of indoor localization using movement trajectories, pedestrian dead-reckoning (PDR) and Graph Simultaneous Localization and Mapping (GraphSLAM). We will present an improved approach for processing measurements from smartphones’ integrated sensors, more specifically accelerometers and gyroscopes. Next, we will integrate the smartphone sensors’ measurements into a pedestrian GraphSLAM optimization problem. Finally, we present a new collaborative framework for optimizing multiple users’ movement trajectories using Bluetooth Low Energy (BLE) detection between smartphones.

Bio: Mahi Abdelbar graduated with honors and received the M.S. degree from the Electronics and Communications Department at the Engineering Faculty, Mansoura University in Egypt. She recently defended her PhD at the Wireless@VT lab, Bradley Department of Electrical and Computer Engineering at Virginia Tech. Her research interests include indoors positioning and localization, distributed signal detection and classification, sensor fusion, and machine learning algorithms.


Date: February 16, 2018

Speaker: Atieh R. Khamesi, Visiting Ph.D. student, Wireless@VT

Time: 2:40 - 3:40. Lavery Hall, Room 330

Title: : Energy Harvesting and Cell Zooming in Heterogeneous Random Cellular Networks

Abstract: In this talk, we investigate network efficiency and green communications in heterogeneous cellular networks (HetNets) as a promising network structure for the 5G using stochastic geometry. We introduce two Cell Zooming (CZ) techniques applying to HetNets. With focus on green communications, we present a K−tier HetNet in which BSs are only powered by energy harvesting. Despite the uncertain nature of energy arrivals, combining two CZ techniques, namely telescopic and ON/OFF scenarios, enables us to achieve higher network performance in terms of the coverage and blocking probabilities while reducing the total power consumption and increasing the energy and spectral efficiencies.

Bio: Atieh R. Khamesi is a Ph.D. student at University of Padova, Italy, advised by Prof. Michele Zorzi. She is currently a visiting student in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. She received her B.Sc. degree in Electrical Engineering from Ferdowsi University of Mashhad, Iran in 2011 and M.Sc. degree in Communication systems from University of Tehran, Iran in 2014. She was visiting researcher at University of Padova from Sep. 2013 to Feb. 2014. Her research interests include green communication, energy harvesting and stochastic geometry.


Date: February 9, 2018

Speaker: Peter Delos, Technical Lead at Analog Devices Inc., Aerospace and Defense Group

Time: 2:40 - 3:40. Lavery Hall, Room 330

Title: Phased Arrays: Fundamentals and the Enabling RF Electronics

Abstract: A large proliferation of digital beamforming phased-array technology has emerged in recent years. The technology has been spawned by both military and commercial applications fueled by the rapid advancements in RF integration at the component level. This presentation will review the radio frequency electronics behind the antenna elements making the phased array advancements possible. Benefits and challenges of varied architecture choices will be described along with technology advancements enabling the next generation of digital beamforming phased arrays.

Bio: Peter Delos is a Technical Lead at Analog Devices, Inc., in the Aerospace and Defense Group. He received his BSEE from Virginia Tech in 1990 and MSEE from NJIT in 2004. He has over 25 years of industry experience. Most of his career has been spent designing advanced RF/Analog systems at the architecture level, PWB level, and IC level. His career includes various positions in the Naval Nuclear Power submarine program, and at Lockheed Martin, Moorestown, NJ, working on multiple Radar and EW programs. In 2016, he accepted his current position with Analog Devices in Greensboro, NC. He is focused on miniaturizing high performance Receiver, Waveform Generator, and Synthesizer designs for Phased Array applications.


Date: January 26, 2018

Speaker: Prof. Lingjia Liu, Associate Professor, Wireless@VT, Department of ECE, Virginia Tech

Time: 2:40 - 3:40. Lavery Hall, Room 330

Title: Rateless Coding-based Cooperative Transmission/Routing for Wireless Networks

Abstract: Cooperation among the nodes of wireless networks can increase communication reliability, reduce energy consumption, and decrease latency. The possible improvements are even more significant when nodes perform mutual-information accumulation, e.g., by using rateless codes. In this talk, we investigate communication problems in such networks. That is, we investigate how advanced physical (PHY) layer coding techniques (e.g. rateless coding) would impact various network layers of a wireless network (e.g., resource allocation and routing strategies). Rateless coding-based cooperative communication for 1D line network, 2D grid network, 2D random network, and dynamic spectrum access network will be presented and hardware demo using USRP N210s will be discussed. All these findings suggest that rateless coding-based cooperative routing can significantly improve network performance in an efficient and robust way.

Bio: Dr. Lingjia Liu is an Associate Professor in the ECE Department at Virginia Tech (VT). He received the B.S. degree in Electronic Engineering from Shanghai Jiao Tong University, Shanghai, China and Ph.D. degree in Electrical Engineering from Texas A&M University, College Station, Texas. He spent the summer of 2007 and spring of 2008 in the Mitsubishi Electric Research Laboratory (MERL). Prior to joining VT, he was an Associate Professor in the EECS Department at the University of Kansas (KU). He spent 3+ years working in the Standards & Mobility Innovation Lab of Samsung Research America (SRA) where he received Global Samsung Best Paper Award twice (in 2008 and 2010 respectively). He was leading Samsung’s efforts on multiuser MIMO, coordinated multipoint (CoMP), and heterogeneous networks in LTE/LTE-Advanced standards.

Dr. Liu is currently an Editor for the IEEE Trans. Wireless Commun., an Editor for the IEEE Trans. Commun. and Associate Editor for the EURASIP J. on Wireless Commun. and Netw. and Wileys Intl J. on Commun. Systems. His general research interests mainly lie in emerging technologies for 5G cellular networks including machine learning for wireless networks, massive MIMO, massive MTC communications, and mmWave communications. Dr. Liu received Air Force Summer Faculty Fellow from 2013 to 2017, Miller Scholar at KU in 2014, Miller Professional Development Award for Distinguished Research at KU in 2015, and 2016 IEEE GLOBECOM Best Paper Award.