R. Michael Buehrer


  • Hello and welcome to the official website of R. Michael Buehrer's research group.

    This website serves as the information gateway to the latest and greatest research being performed by his M.Sc. and Ph.D. students in the Mobile and Portable Radio Research Group (MPRG) within the Wireless@VT umbrella.

    Please feel free to browse around at your leisure. Should you desire to contact Dr. Buehrer, please find his contact information here.

    Lastly, we appreciate you stopping by - thank you for your interest!

  • News

    Jeff Poston wins 2nd place in Torgersen Graduate Research Awards, May 02, 2017

    Jeff Poston wins 2nd place in 2017 Paul E. Torgersen Graduate Student Research Excellence Award. Congratulations Jeff. You make us proud.

    Buehrer named IEEE Fellow, Nov 29, 2016

    Mike Buehrer has been named IEEE Fellow for his contributions to wideband signal processing in communications and geolocation. His research focuses on wireless communications, ultra-wideband communication and sensing systems, and cognitive radio.

    Our Research in Press

    Our recent research on blindly attacking networks was highlighted in the IEEE Xplore Spotlight. More details can be found here.

    Daniel Jakubisin Successfully Defended His PhD Thesis on April 22, 2016.

    Abstract: As wireless communication systems continue to push the limits of energy and spectral efficiency, increased demands are placed on the capabilities of the receiver. At the same time, the computational resources available for processing received signals will continue to grow. This opens the door for iterative algorithms to play an increasing role in the next generation of communication receivers.

    In the context of receivers, the goal of iterative probabilistic processing is to approximate maximum a posteriori (MAP) symbol-by-symbol detection of the information bits and estimation of the unknown channel or signal parameters. The sum-product algorithm is capable of efficiently approximating the marginal posterior probabilities desired for MAP detection and provides a unifying framework for the development of iterative receiver algorithms. However, in some applications the sum-product algorithm is computationally infeasible. Specifically, this is the case when both continuous and discrete parameters are present within the model. Also, the complexity of the sum-product algorithm is exponential in the number of variables connected to a particular factor node and can be prohibitive in multi-user and multi-antenna applications.

    In this dissertation we identify three key problems which can benefit from iterative probabilistic processing, but for which the sum-product algorithm is too complex. They are (1) joint synchronization and detection in multipath channels with emphasis on frame timing, (2) detection in co-channel interference and non-Gaussian noise, and (3) joint channel estimation and multi-signal detection. This dissertation presents the advances we have made in iterative probabilistic processing in order to tackle these problems. The motivation behind the work is to (a) compromise as little as possible on the performance that is achieved while limiting the computational complexity and (b) maintain good theoretical justification to the algorithms that are developed.

    SaiDhiraj Amuru Successfully Defended His PhD Thesis on September 21, 2015.

    Abstract: Spectrum supremacy is a vital part of security in the modern era. In the past 50 years, a great deal of work has been devoted to designing defenses against attacks from malicious nodes (e.g., anti-jamming), while significantly less work has been devoted to the equally important task of designing effective strategies for denying communication between enemy nodes/radios within an area (e.g., jamming). Such denial techniques are especially useful in military applications and intrusion detection systems where untrusted communication must be stopped. In this dissertation, we study these offensive attack procedures, collectively termed as \emph{communication denial}. The communication denial strategies studied in this dissertation are not only useful in undermining the communication between enemy nodes, but also help in analyzing the vulnerabilities of existing systems.

    A majority of the works which address communication denial assume that knowledge about the enemy nodes is available \emph{a priori}. However, recent advances in communication systems creates the potential for dynamic environmental conditions where it is difficult and most likely not even possible to obtain \emph{a priori} information regarding the environment and the nodes that are present in it. Therefore, it is necessary to have cognitive capabilities that enable the attacker to learn the environment and prevent enemy nodes from accessing valuable spectrum, thereby denying communication.

    In this regard, we ask the following question in this dissertation "Can an intelligent attacker learn and adapt to unknown environments in an electronic warfare-type scenario?" Fundamentally speaking, we explore whether existing machine learning techniques can be used to address such cognitive scenarios and, if not, what are the missing pieces that will enable an attacker to achieve spectrum supremacy by denying an enemy the ability to communicate? The first task in achieving spectrum supremacy is to identify the signal of interest before it can be attacked. Thus, we first address signal identification, specifically modulation classification, in practical wireless environments where the interference is often non-Gaussian. Upon identifying the signal of interest, the next step is to effectively attack the victim signals in order to deny communication. We present a rigorous fundamental analysis regarding the attackers performance, in terms of achieving communication denial, in practical communication settings. Furthermore, we develop intelligent approaches for communication denial that employ novel machine learning techniques to attack the victim either at the physical layer, the MAC layer, or the network layer. We rigorously investigate whether or not these learning techniques enable the attacker to approach the fundamental performance limits achievable when an attacker has complete knowledge of the environment. As a result of our work, we debunk several myths about communication denial strategies that were believed to be true mainly because incorrect system models were previously considered and thus the wrong questions were answered.

    Javier Schloemann Successfully Defended His PhD Thesis on August 3, 2015.

    Abstract: Determining the locations of devices in mobile ad-hoc networks (MANETs), wireless sensor networks (WSNs), and cellular networks has many important applications. In MANETs, which are useful in disaster recovery, rescue operations, and military communications, location information is used to enable location-aided routing and geodesic packet forwarding. In WSNs, whose applications include environmental monitoring (e.g., for precision agriculture) and asset tracking in warehouses, not only is location information useful for the self-organization of the network, but in addition, tying locations to the sensor observations is crucial for adding meaning to the sensed data. In cellular networks, location information is used to provide subscribers with location-based services in addition to providing public service answering points with potentially life-saving location information during emergency calls. These applications are largely not new, which is evidenced by the fact that the literature is quite rich with localization studies presented over the span of many years. Because of this, it may be surprising to learn that there is a lack of analyses concerning the fundamental factors impacting localization performance.

    Fundamentally, localization performance depends upon three factors: (i) the number of devices participating in the localization procedure, (ii) the locations of the participating devices, and (iii) the quality of the positioning observations gathered from the participating devices. For the most part, these factors cannot reasonably be considered deterministic. Instead, at any point in time, random effects within a network and its surroundings will determine these factors for individual positioning scenarios. Unfortunately, there are currently no analytical approaches for characterizing localization performance over these random factors. Instead, researchers either provide analytical results for a deterministic set of factors or use complex system-level simulations to obtain general performance insights. While the latter certainly averages over the random factors, the validity of the results is limited by the simulation assumptions. Any change in a network parameter requires running a new time-consuming simulation.

    In this dissertation, we address current deficiencies in several ways. We present a new model for tractably analyzing network localization fundamentals. This is demonstrated through fundamental analyses of hearability and geometry. Further, collaboration among non-reference devices has recently garnered increasing interest from the research community as a means to (i) improve positioning accuracy and (ii) improve positioning availability. We present fundamental analyses of both of these potential benefits. As a result of our work, we not only characterize several key performance metrics, we also demonstrate that there exist new tractable ways to analyze localization performance.

    Kevin McDermott Successfully Defended His Master's Thesis

    Abstract: The ability to find the location of a mobile user has become of utmost importance. The demands of first responders necessitates the ability to accurately identify the location of an individual who is calling for help. Their response times are directly influenced by the ability to locate the caller. Thus, applications such as Enhanced 911 and other location-based services warrant the ability to quickly and accurately calculate location. The FCC has also put in place a timeline for indoor location accuracy requirements that must be met by the mobile communications service providers. In order to meet these requirements, there are many means of performing indoor geolocation that require research; in this thesis two specific methods of identifying the location of a user will be investigated.

    In the first part, the indoor localization of a target, whose exact location is unknown, in a LTE network is studied. In this problem the time difference of arrival of the LTE uplink signals sent from the target to an observer are used as the means to estimate the target position. The two-dimensional location of a user is then estimated through the use of a nonlinear least-squares algorithm. To improve this approach, a cooperative localization technique in uplink LTE is proposed in which the User Equipment (UE) communicates with base stations as well as other handsets. Through simulated results it is shown that utilizing collaboration can improve location estimation and outperform non-collaborative localization.

    In the second part, the indoor localization of a target, focusing on its third dimension or elevation, is studied through the use of barometric pressure sensors in mobile handsets. Finding the third dimension of location, or the correct height above the ground level which equates to the floor in a building that a UE is on, cannot be performed with two-dimensional measurement models. For this problem, the pressure sensors are used to accurately find an immediate pressure measurement and allow for the altitude of a handset to be calculated. This altitude can be translated into an estimation for a specific floor of a building given the use of a ground floor pressure reference. Through simulation results it is then shown that the accuracy of third dimension or indoor-floor localization can be improved with the use of collaborative pressure sensors of other mobile handsets.

    Michael Buehrer is a Co-Chair for Localization Workshop at GLOBECOM 2015

    Dr. Buehrer is a co-chair for the workshop on localization for indoors, outdoors, and emerging networks (LION) at Globecom 2015. The workshop aims to attract recent work in all areas of localization, with an emphasis on physical-layer techniques and on the recent position location trends. More details are available here. Please consider submitting your papers.

    Michael Buehrer Named VT Scholar of the Week

    The Office of the Vice President for Research recognizes R. Michael Buehrer as a Virginia Tech Scholar of the Week. His research includes wireless communications, ultra-wideband communication and sensing systems, cellular and personal communications, multiuser detection, "intelligent" antennas, and cognitive radio. The director of Wireless@Virginia Tech, Buehrer advances world-changing technologies in wireless communications, ultra-wideband communication and sensing systems, cellular and personal communications, multiuser detection, “intelligent” antennas, and cognitive radio. More details are available here.

    Reza Successfully Defended His Ph.D. Thesis on December 8, 2014

    Abstract: With the rapid development of wireless technologies, the demand for positioning services has grown dramatically over the past three decades. The Global Positioning System (GPS) is widely used in wireless devices for positioning purposes. However, in addition to having bulky and expensive equipment, GPS receivers do not operate properly in dense and indoor environments. Difficulties in using GPS lead us to use sensor localization in which the position information is obtained from the measurements collected within the network without the aid of external resources. Sensor localization has been a great topic of interest during past decades. Although many positioning algorithms have been developed previously in the literature, positioning is still a challenging task. There are many factors that can affect the positioning performance if they are neglected or not treated properly. These factors introduce many nuisance parameters which need to be either estimated or considered when the location is estimated.

    In this work, we exploit cooperative localization as a recent and trending technology and semidefinite programming (SDP) as a powerful tool in our research. Cooperative localization has several advantages over the traditional noncooperative localization in terms of positioning accuracy and localizability. Cooperation is also highly beneficial for networks with few anchor nodes and low communication range. On the other hand, SDP provides an alternative solution to the optimal maximum-likelihood (ML) estimation. Unlike in the ML estimator, convergence to the global minimum is guaranteed in SDP. It also has significantly lower complexity especially for cooperative networks in exchange for small performance degradation. Using these two concepts, four open problems within the area of cooperative localization and tracking in the presence of nuisance parameters are addressed. In particular, we focus on cooperative received signal strength-based localization when the propagation parameters including path-loss exponent and transmit powers are unknown. Cooperative time-of-arrival-based localization in harsh environments in the presence of severe non-line-of-sight (NLOS) propagation is also investigated. Cooperative localization in asynchronous networks is studied where the clock parameters are considered as nuisance parameters and the focus is on a joint synchronization and localization approach. Lastly, source tracking in NLOS environments is studied where source nodes are mobile and their status changes rapidly from LOS to NLOS and vice versa.

    Michael Buehrer Receives 2014 Dean's Award for Teaching Excellence

    Michael Buehrer received the Dean's Award for Teaching Excellence from the College of Engineering. The award was presented during the annual College of Engineering reception and awards ceremony on May 19, 2014. Buehrer has developed several new ECE courses, including Spread Spectrum Communications and Multi-Channel Communications (doctoral-level course). Multi-Channel Communications which is only taught in a few top universities covers the fundamentals of the most recent communications technology such as LTE and WiMAX. He consistently earns positive student evaluations. More details are available here.

    DARPA Spectrum Challenge

    On September 11-12, 2013, VT CogRad represented the Wireless@VT research group in the DARPA Spectrum Challenge's Preliminary Tournament. Along with 17 other teams, VT CogRad designed a software-defined radio to compete in the competitive and cooperative tournaments. VT CogRad team including SaiDhiraj Amuru, Daniel Jakubisin, Jeffrey Poston, and R. Michael Buehrer placed fourth in the competitive challenge. In the competitive match VT CogRad's design successfully created interference to the opposing team while rapidly transmitting packets of its own which allowed the team to win four rounds before being eliminated by the eventual second place team. VT CogRad successfully qualified for the tournament in April by passing the Hurdles with a 11th place score. Teams now have the opportunity to improve their strategy before competing in the DARPA Spectrum Challenge's Final Tournament which will be held in March 2014. In the Final Tournament, the DARPA Spectrum Challenge plans to award $50,000 to the winners of the competitive and cooperative matches. Full Preliminary Tournament results are available here.

    The 1st Contest on Localization Algorithms

    Our research team including Reza Monir Vaghefi, Javier Schloemann, and R. Michael Buehrer won the 1st Contest on Localization Algorithms in Dresden on 19th of March. The contest was hosted by the 10th Workshop on Positioning, Navigation and Communication 2013 (WPNC'13) in Dresden, Germany, March 20-21, 2013 in cooperation with BUTLER FP7 EU project. The participants compared their localization algorithms based on given distance datasets and evaluation metrics.

    DARPA Spectrum Challenge

    SaiDhiraj Amuru, Daniel Jakubisin, Jeffrey Poston, and R. Michael Buehrer, the members of the team VT CogRad, were qualified for the DARPA Spectrum Challenge tournaments. 90 teams registered as Challenge entrants, with participants from around the world. However, only 15 teams were selected as contestants for the Challenge tournaments where VT CogRad ranked 11th. The DARPA Spectrum Challenge is a competition to demonstrate a radio protocol that can best use a given communication channel in the presence of other dynamic users and interfering signals.