About

R. Michael Buehrer is a professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. He holds a Ph.D. from Virginia Tech, earned in 1996, and has completed master's and bachelor's degrees at The University of Toledo in 1993 and 1991, respectively. His research interests include communications systems, cellular and personal communications, wireless communications, ultra-wideband communication and sensing systems, code division multiple access, spread-spectrum, multiuser detection, adaptive antennas, space-time coding, and radio resource control. He has been recognized as an IEEE Fellow in 2017 for his contributions to wideband signal processing in communications and geolocation.

Research topics

• Artificial Intelligence
• Computer Science
• Machine Learning
• Computer network
• Simulation
• Telecommunications
• Real-time computing
• Mathematics
• Computer vision
• Geology

Selected publications

• Age of Positioning with Stochastic Motion Models

  IEEE Transactions on Vehicular Technology · 2026-01-01

  articleOpen access

  Age of Information (AoI) is a key metric used for evaluating data freshness in communication networks, particularly in systems requiring real-time updates. In positioning applications, maintaining low AoI is critical for ensuring timely and accurate position estimation. This paper introduces an age-informed metric, which we term as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Age of Positioning (AoP)</i>, that captures the temporal evolution of positioning accuracy for agents following random trajectories and sharing sporadic location updates. We use the random waypoint (RWP) mobility model, which captures stochastic user movements through waypoint-based trajectories. We derive closed-form expressions for this metric under various queuing disciplines and different modes of operation of the agent. The analytical results are verified with numerical simulations, and the existence of optimal operating conditions is demonstrated.

  PublisherOA PDFDOI

• Diffraction-Aided Wireless Positioning

  IEEE Transactions on Wireless Communications · 2025-02-10 · 3 citations

  article

  Wireless positioning in Non-Line-of-Sight (NLoS) scenarios presents significant challenges due to multipath effects that lead to biased measurements and reduced positioning accuracy. This paper revisits electromagnetic field theory related to diffraction and in the context of wireless positioning and proposes a novel positioning technique that greatly improves accuracy in NLoS environments dominated by diffraction. The method is applied to a critical public safety use case: precisely locating at-risk individuals within buildings, with a particular focus on improving 3D positioning and z-axis accuracy. By leveraging the Geometrical Theory of Diffraction (GTD), the approach introduces an innovative NLoS path length model and a new NLOS positioning technique. Using Fisher information analysis, we establish the conditions required for 3D positioning and derive lower bounds on positioning performance for both 3D and z-axis estimates for the proposed NLOS positioning technique. Additionally, we propose an algorithmic implementation of the proposed NLoS positioning method using non-linear least squares estimation, which we term D-NLS. The positioning performance of our proposed NLOs positioning technique is validated using an extensive ray-tracing simulation. The numerical results highlight the superiority of our approach in outdoor-to-indoor environments, which directly estimates NLoS path lengths and delivers significant performance enhancements over existing methods for both 3D and z-axis positioning scenarios.

  PublisherDOI

• A New Statistical Approach to Calibration-Free Localization Using Unlabeled Crowdsourced Data

  ArXiv.org · 2025-04-04

  preprintOpen accessSenior author

  Fingerprinting-based indoor localization methods typically require labor-intensive site surveys to collect signal measurements at known reference locations and frequent recalibration, which limits their scalability. This paper addresses these challenges by presenting a novel approach for indoor localization that utilizes crowdsourced data without location labels. We leverage the statistical information of crowdsourced data and propose a cumulative distribution function (CDF) based distance estimation method that maps received signal strength (RSS) to distances from access points. This approach overcomes the limitations of conventional distance estimation based on the empirical path loss model by efficiently capturing the impacts of shadow fading and multipath. Compared to fingerprinting, our unsupervised statistical approach eliminates the need for signal measurements at known reference locations. The estimated distances are then integrated into a three-step framework to determine the target location. The localization performance of our proposed method is evaluated using RSS data generated from ray-tracing simulations. Our results demonstrate significant improvements in localization accuracy compared to methods based on the empirical path loss model. Furthermore, our statistical approach, which relies on unlabeled data, achieves localization accuracy comparable to that of the supervised approach, the $k$-Nearest Neighbor ($k$NN) algorithm, which requires fingerprints with location labels. For reproducibility and future research, we make the ray-tracing dataset publicly available at [2].

  PublisherOA PDFDOI

• Interference Mitigation using U-Net Autoencoder based system

  ArXiv.org · 2025-12-15

  preprintOpen accessSenior author

  This paper proposes a U-Net-based autoencoder framework for mitigating interference in communication signals corrupted by noise and diverse interference sources. The approach targets scenarios involving both signal-plus-noise and signal-plus-interference-plus-noise mixtures, including sinusoidal interferers, LFM chirps, QPSK interferers with different sampling rates, and modulated interference such as QAM. The U-Net architecture leverages multiscale feature extraction and skip connections to preserve fine-grained temporal structure while suppressing interference components. Performance is evaluated using bit error rate and compared against conventional cancellation methods. Results show that the proposed method consistently outperforms traditional techniques in low- and mid-SIR regimes, while remaining competitive at high SIRs. Additional experiments examine the autoencoder's behavior under model mismatch conditions such as carrier offset and colored noise. The study demonstrates that multiscale neural architectures provide a flexible and effective platform for interference mitigation across a wide range of interference types.

  PublisherOA PDFDOI

• Multi-Mode Radar Network Control With Restless Contextual Combinatorial Multi-Armed Bandits

  IEEE Transactions on Radar Systems · 2025-11-18

  articleSenior author

  In congested or contested spectrum, radar is costly to operate due to high power usage, low spectral efficiency, and low covertness compared to passive sensors. For this reason this work proposes a multi-mode radar sensing strategy, in which the sensors can choose between a monostatic radar mode and a passive Electronic Support Measure (ESM) spectrum sensing mode. In ESM mode, a target is localized with a network of multi-mode sensors, which creates opportunities to reduce radar measurements. Radar and ESM measurements are rigorously compared using the Cramer-Rao bound to quantify the localization error of each mode. The best mode for each sensor is chosen using a Restless Contextual Combinatorial Multi-Armed Bandit (RCC-MAB) online learning algorithm. The RCC-MAB increases the flexibility of the network by adapting to the target in real-time based on recent radar and ESM measurements. Two variants, the ϵ-Greedy and Covert RCC-MABs, were created to fulfill different tracking objectives. The ϵ-Greedy RCC-MAB variant seeks to minimize the tracking error by selecting the best sensing modes based on the quality of previous measurements and the current context of the tracking filter. The Covert RCC-MAB variant significantly reduces radar usage to stay covert, or minimize access to a shared spectrum, by only exploring radar measurements when the tracking error approaches a predefined maximum error. The ϵ-Greedy RCC-MAB consistently achieved the lowest tracking error of the tested mode controllers, 58% and 16% lower than a single-mode radar and ESM network, respectively, when the radio emissions of opportunity were available during 50% of measurement opportunities. In the same scenario, the Covert RCC-MAB had 55% lower tracking error than single-mode radar, while using 82% less radar than the ϵ-Greedy RCC-MAB.

  PublisherDOI

• Interference Mitigation Recommender System using U-Net Autoencoders

  ArXiv.org · 2025-12-15

  preprintOpen accessSenior author

  Building on the previous work on interference mitigation, this paper introduces a modular recommender system that automatically selects the most effective interference mitigation strategy based on the interference characteristics present in the received signal. The system integrates three key stages: an SPS classifier module, a SIR predictor, and a bank of specialized U-Net autoencoders designed for different interference conditions. The classification block identifies the parameters required for cancellation. The recommender then directs the signal to the appropriate mitigation model, optionally incorporating SIR-based decisions for scenarios where successive interference cancellation may be advantageous. Experiments conducted across diverse SIR levels and modulation environments show that the recommender strategy improves robustness and reduces BER compared to using any single mitigation method alone. The results demonstrate the potential of adaptive, model-selective architectures to enhance interference resilience in dynamic communication environments.

  PublisherOA PDFDOI

• Experimental Demonstration of Robust Distributed Wireless Clock Synchronization

  ArXiv.org · 2025-09-29

  preprintOpen accessSenior author

  Distributed wireless clock synchronization is essential for aligning the clocks of distributed transceivers in support of joint transmission and reception techniques. One recently explored method involves synchronizing distributed transceivers using a two-tone waveform, where the tones are separated in frequency by a clock (frequency) reference signal. Prior research has demonstrated frequency accuracy better than 1 Hz; however, this approach remains vulnerable to both intentional and unintentional interference. In this demonstration, we present a robust, frequency-hopped two-tone waveform that enables transceivers to extract the reference signal without prior knowledge of the exact frequency at which the tones are transmitted.

  PublisherOA PDFDOI

• A Novel Positioning Framework: Two-Stage Weighted Projection with NLOS Bias Modeling

  2025-06-08 · 1 citations

  article

  Traditionally, parallel projection methods (PPMs) have been primarily applied to time-of-arrival (TOA)-based localization. In this paper, we present a series of advancements to PPMs that significantly extend their capabilities and improve positioning accuracy. Specifically, we introduce: 1) a reformulated iterative PPM (IPPM) tailored for time-difference-of-arrival (TDOA)-based localization, and 2) a novel weighted PPM (WPPM), where a two-stage refining approach enhances positioning accuracy, particularly in challenging conditions such as low signal-to-noise ratio (SNR) or high geometric dilution of precision (GDOP). To rigorously evaluate the performance of these enhancements, we develop a comprehensive system-level analytical framework compliant with the 3rd Generation Partnership Project (3GPP) standards. This framework incorporates key 5G new radio (NR) physical layer aspects alongside large-scale and small-scale fading effects. As part of our analysis, we derive a generalized Cramér-Rao lower bound (CRLB) to characterize ranging and positioning error bounds in the presence of additive white Gaussian noise (AWGN) and multipath propagation. Additionally, we propose a novel method to model non-line-of-sight (NLOS) bias induced by multipath, incorporating underlying radio propagation characteristics and SNR. The derived ranging errors are utilized to conduct Monte-Carlo simulations to evaluate the performance of the proposed enhancements and benchmark them against CRLB. Our results demonstrate that the novel WPPM consistently outperforms the weighted non-linear least squares (WNLS) method under non-ideal conditions such as low SNR or high GDOP.

  PublisherDOI

• A New Statistical Approach to the Performance Analysis of Vision-based Localization

  ArXiv.org · 2025-01-30

  preprintOpen accessSenior author

  Many modern wireless devices with accurate positioning needs also have access to vision sensors, such as a camera, radar, and Light Detection and Ranging (LiDAR). In scenarios where wireless-based positioning is either inaccurate or unavailable, using information from vision sensors becomes highly desirable for determining the precise location of the wireless device. Specifically, vision data can be used to estimate distances between the target (where the sensors are mounted) and nearby landmarks. However, a significant challenge in positioning using these measurements is the inability to uniquely identify which specific landmark is visible in the data. For instance, when the target is located close to a lamppost, it becomes challenging to precisely identify the specific lamppost (among several in the region) that is near the target. This work proposes a new framework for target localization using range measurements to multiple proximate landmarks. The geometric constraints introduced by these measurements are utilized to narrow down candidate landmark combinations corresponding to the range measurements and, consequently, the target's location on a map. By modeling landmarks as a marked Poisson point process (PPP), we show that three noise-free range measurements are sufficient to uniquely determine the correct combination of landmarks in a two-dimensional plane. For noisy measurements, we provide a mathematical characterization of the probability of correctly identifying the observed landmark combination based on a novel joint distribution of key random variables. Our results demonstrate that the landmark combination can be identified using ranges, even when individual landmarks are visually indistinguishable.

  PublisherOA PDFDOI

• On the Use of Björck Sequences in LEO-based PNT Systems

  ArXiv.org · 2025-05-31

  preprintOpen access

  In this paper, we investigate the use of Björck sequences, a class of constant amplitude zero autocorrelation (CAZAC) sequences, as a potential candidate for the design of positioning reference signals (PRS) in Low Earth Orbit (LEO)-based positioning, navigation, and timing (PNT) systems. Unlike legacy systems such as Global Navigation Satellite Systems (GNSS) or terrestrial networks (TNs), LEO-based systems experience large Doppler shifts and delay spreads, where traditional orthogonalization methods become ineffective. Compared to commonly used sequences such as Gold and Zadoff-Chu (ZC), Björck sequences offer improved ambiguity function behavior, nearly ideal autocorrelation, greater resilience to interference, and accurate delay estimation in high Doppler environments. We further propose a novel sequence construction method to extend Björck sequences to non-prime lengths while minimizing cyclic autocorrelation. Focusing on LEO-based non-terrestrial network (NTN) localization, we evaluate positioning accuracy under various interference conditions, comparing the performance of Björck sequences against Gold sequences, which are traditionally used for PRS generation. While Björck sequences demonstrate strong performance in Doppler-rich environments, we identify an inherent Doppler-dependent behavior that may lead to sequence misidentification. To mitigate this, we propose two strategies: 1) leveraging the availability of a coarse Doppler estimate and 2) employing sequence subset selection to ensure sufficient separation between sequences to account for maximum Doppler uncertainty. Finally, we present scalable sequence reuse strategies for large LEO constellations.

  PublisherOA PDFDOI

Recent grants

• U-PoLo Net: UWB-Based Position Location Networks for Harsh Environments

  NSF · $198k · 2005–2008

Frequent coauthors

• Harpreet S. Dhillon

  Virginia Tech

  41 shared

• SaiDhiraj Amuru

  Indian Institute of Technology Hyderabad

  35 shared

• Anthony F. Martone

  United States Army Combat Capabilities Development Command

  31 shared

• B.D. Woerner

  West Virginia University

  28 shared

• Charles E. Thornton

  Virginia Tech

  25 shared

• Reza Monir Vaghefi

  21 shared

• Javier Schloemann

  Northrop Grumman (United States)

  20 shared

• Jeffrey H. Reed

  Virginia Tech

  17 shared

Labs

• Bradley Department of Electrical and Computer EngineeringPI

Awards & honors

• IEEE Fellows (2017)