About

Milica Stojanovic is a professor of electrical and computer engineering at Northeastern University College of Engineering. Her research focuses on wireless communications, particularly in the development of advanced channel sounding technologies for next-generation wireless networks, including 6G. She has contributed to the characterization of sub-terahertz MIMO channels, exemplified by her work on a novel ultrabroadband channel sounder operating at D-band frequencies (110-170 GHz), which is critical for future wireless communication systems. Her work has been recognized with awards such as the Best Paper Award at IEEE Globecom 2023, where she was part of a team that developed innovative solutions for sub-THz MIMO channel measurement and analysis.

Research topics

• Computer Science
• Telecommunications
• Artificial Intelligence
• Engineering
• Electronic engineering
• Arithmetic
• Computer architecture
• Mathematics
• Physics
• Computer network
• Algorithm

Selected publications

• Underwater Acoustic Channel Library

  Zenodo (CERN European Organization for Nuclear Research) · 2026-04-15

  datasetOpen accessSenior author

  This dataset contains measured underwater acoustic channel impulse responses (CIRs) collected during at-sea experiments spanning diverse geographic locations, environmental conditions, and system configurations. Each CIR captures how a transmitted acoustic signal is modified by the ocean environment as a function of propagation delay, and evolves over time to reflect the non-stationary nature of the underwater channel. The collection currently includes eight channel datasets, identified by color labels (Blue, Red, Yellow, Purple, Green, Black, Pink, Brown). These span locations including the North Atlantic, Singapore, Hawaii, Norway, Japan, the Mariana Trench, and the Pacific Ocean, with transmission ranges from tens of meters to thousands of kilometers, center frequencies from 75 Hz to 25 kHz, and receiver configurations ranging from single hydrophones to vertical, cross, and circular arrays. Each channel is stored as a compressed complex-baseband channel matrix in MATLAB format (.mat), accompanied by a phase vector that encodes the time-varying delay drift suppressed during compression. Full reconstruction instructions and ready-to-use toolboxes in MATLAB and Python are available at https://github.com/uwa-channels. This library is intended to support reproducible research and benchmarking in underwater acoustic communications, channel modeling, and signal processing. Researchers can apply the provided channels directly to signals of their choice using the companion software.

  PublisherDOI

• Underwater Acoustic Channel Library

  Zenodo (CERN European Organization for Nuclear Research) · 2026-04-15

  datasetOpen accessSenior author

  This dataset contains measured underwater acoustic channel impulse responses (CIRs) collected during at-sea experiments spanning diverse geographic locations, environmental conditions, and system configurations. Each CIR captures how a transmitted acoustic signal is modified by the ocean environment as a function of propagation delay, and evolves over time to reflect the non-stationary nature of the underwater channel. The collection currently includes eight channel datasets, identified by color labels (Blue, Red, Yellow, Purple, Green, Black, Pink, Brown). These span locations including the North Atlantic, Singapore, Hawaii, Norway, Japan, the Mariana Trench, and the Pacific Ocean, with transmission ranges from tens of meters to thousands of kilometers, center frequencies from 75 Hz to 25 kHz, and receiver configurations ranging from single hydrophones to vertical, cross, and circular arrays. Each channel is stored as a compressed complex-baseband channel matrix in MATLAB format (.mat), accompanied by a phase vector that encodes the time-varying delay drift suppressed during compression. Full reconstruction instructions and ready-to-use toolboxes in MATLAB and Python are available at https://github.com/uwa-channels. This library is intended to support reproducible research and benchmarking in underwater acoustic communications, channel modeling, and signal processing. Researchers can apply the provided channels directly to signals of their choice using the companion software.

  PublisherDOI

• Continuously Optimizing Radar Placement With Model-Predictive Path Integrals

  IEEE Transactions on Aerospace and Electronic Systems · 2025-01-13

  article

  Continuously optimizing sensor placement is essential for precise target localization in various military and civilian applications. While information theory has shown promise in optimizing sensor placement, many studies oversimplify sensor measurement models or neglect dynamic constraints of mobile sensors. To address these challenges, we employ a range measurement model that incorporates radar parameters and radar–target distance, coupled with model-predictive path integral control to manage complex environmental obstacles and dynamic constraints. We compare the proposed approach against stationary radars or simplified range measurement models based on the root-mean-squared error (RMSE) of the cubature Kalman filter estimator for the targets' state. In addition, we visualize the evolving geometry of radars and targets over time, highlighting areas of highest measurement information gain, demonstrating the strengths of the approach. The proposed strategy outperforms stationary radars and simplified range measurement models in target localization, achieving a 38%–74% reduction in the mean RMSE and a 33%–79% reduction in the upper tail of the 90% highest density interval over 500 Monte Carlo trials across all time steps.

  PublisherDOI

• Path-Specific Angle and Doppler Tracking in Dynamic Underwater Channels

  2025-06-16

  article

  We present a method for path-specific angle and Doppler tracking in the context of a single-input multiple-output single-carrier system for under-water acoustic communications. We address scenarios in which the multipath channel exhibits different Doppler distortions across different propagation paths. The proposed algorithm builds upon gradient-based beamforming methods to track the path angles and isolate the corresponding signal contributions. The isolated multi path signals are fed to a multi-channel fractionally-spaced decision-feedback equalizer (DFE), which couples a phase-locked loop (PLL) with a delay-locked loop (DLL) per beamformed path to perform adaptive resampling. The advantage of such an approach is not only in the ability to compensate for path-specific Doppler scaling, but also in reducing the overall equalizer complexity, which is now dictated by delay spreading on an individual path rather than the entire multipath spread. The performance is demonstrated through simulations of highly mobile scenarios with speeds on the order of 20 m/ s, showing excellent results in terms of data detection mean squared error (MSE).

  PublisherDOI

• A Method for Adaptive Channel Estimation

  2025-06-16

  article

  We present a method for adaptive acoustic communication channel estimation. This method utilizes the data detection mean-squared error (MSE) to simultaneously estimate the channel and track the Doppler-induced delay drift and phase offset by employing a delay-locked loop (DLL) coupled with a phase-locked loop (PLL). Through simulations, we compare the proposed method with conventional approaches, specifically the least mean squares (LMS) and recursive least squares (RLS) algorithms, demonstrating excellent results in terms of channel estimation normalized MSE. Using experimental data, we evaluate the proposed method in mobile scenarios, showcasing the effective tracking operation of the Doppler-induced delay drifts.

  PublisherDOI

• Space-code division multiple access for broadband acoustic networks

  Computer Networks · 2024-04-10 · 4 citations

  articleOpen accessSenior author

  We present an investigation into the design of an acoustic communication network, where multiple users are distributed across space and transmit and receive simultaneously in the same band to and from a common base station. Specifically, we focus on a system that utilizes orthogonal frequency division multiplexing as a modulation method, and allows users to transmit and receive in either synchronous or asynchronous fashion. To distinguish between users on the uplink, the base station employs a combination of code-division and space-division multiple access. The base station iteratively steers a beam to each stable propagation path of the desired user’s channel while placing nulls in the direction of other paths, as well as in the directions of interfering users. Finally, the multiple paths of the desired user are recombined before data detection. The process is repeated for each user. Broadband beamforming is employed to account for the broadband nature of acoustic signals. The beamformer coefficients on each carrier depend on the angles of signal arrivals, which are estimated during the uplink transmission and used to construct both the uplink and the downlink beamformer. On the downlink, the base station utilizes angle information to assemble beamforming weights and point in the direction of stable paths of the users. It superimposes multiuser signals and transmits the sum signal to the users. The signal intended for a given user reaches only that user, requiring just a simple detector. Each user is equipped by a single-element transducer. To demonstrate the design concepts, we conducted simulations using a shallow water channel model and performed experimental over-the-air tests in an indoor environment using an acoustic communications testbed. The results were excellent, thereby encouraging future implementations in practical systems.

  PublisherDOI

• Underwater acoustic communications

  Nature Reviews Electrical Engineering · 2024-12-04 · 65 citations

  articleSenior author
  PublisherDOI

• Continuously Optimizing Radar Placement with Model Predictive Path Integrals

  arXiv (Cornell University) · 2024-05-29

  preprintOpen access

  Continuously optimizing sensor placement is essential for precise target localization in various military and civilian applications. While information theory has shown promise in optimizing sensor placement, many studies oversimplify sensor measurement models or neglect dynamic constraints of mobile sensors. To address these challenges, we employ a range measurement model that incorporates radar parameters and radar-target distance, coupled with Model Predictive Path Integral (MPPI) control to manage complex environmental obstacles and dynamic constraints. We compare the proposed approach against stationary radars or simplified range measurement models based on the root mean squared error (RMSE) of the Cubature Kalman Filter (CKF) estimator for the targets' state. Additionally, we visualize the evolving geometry of radars and targets over time, highlighting areas of highest measurement information gain, demonstrating the strengths of the approach. The proposed strategy outperforms stationary radars and simplified range measurement models in target localization, achieving a 38-74% reduction in mean RMSE and a 33-79% reduction in the upper tail of the 90% Highest Density Interval (HDI) over 500 Monte Carl (MC) trials across all time steps. Code will be made publicly available upon acceptance.

  PublisherOA PDFDOI

• Generating Underwater Acoustic Communication Channel Impulse Responses Using a Diffusion Model

  2024-09-03

  article

  Underwater acoustic communication data is expensive to collect, often yielding experimental data sets that are sufficient for proof-of-concept analysis, but are insufficient for applications that demand orders of magnitude more channel realizations, such as data-driven methods for outage-capacity analysis, network simulation, or statistical regression of new communication algorithms. For such applications, a realistic synthetic dataset is a necessity for the successful generalization of data-driven underwater acoustic (UWA) communication systems. This paper investigates the application of diffusion models for UWA data augmentation for such systems. Diffusion models, as opposed to physics-based models such as BELLHOP or other acoustic propagation tools, extract the essential characteristics of the data without explicit knowledge of environmental parameters. We demonstrate the capability of such models by generating data whose multipath structure and spatiotemporal correlation match those of the Kauai ACOMMS MURI 2011 (KAM11) experiment.

  PublisherDOI

• Guessing Random Additive Noise Decoding for Underwater Acoustic Communications

  2024-10-28

  articleOpen accessSenior author
  PublisherDOI

Recent grants

• NeTS: Large: Collaborative Research: Exploration and Exploitation in Actuated Communication Networks

  NSF · $369k · 2012–2017

• Collaborative Research: ITR-(ASE)+(DMC_INT): New Methods for the Exploration of Deep-Sea Hydrothermal Vents with Multiple Autonomous Underwater Robotic Vehicles

  NSF · $328k · 2004–2008

• Collaborative NETS-NECO: Wireless Underwater Multi-tiered Acoustic Networks (WUMAN)

  NSF · $250k · 2008–2013

• Collaborative Research: CRI: CRD: Open Research Testbed for Underwater Ad Hoc and Sensor Networks

  NSF · $100k · 2008–2013

• Collaborative Research: NeTS-NOSS: Networking the Digital Ocean

  NSF · $269k · 2005–2009

Frequent coauthors

• Michele Zorzi

  University of Padua

  1450 shared

• Educational Activities

  Institute of Electrical and Electronics Engineers

  1444 shared

• Maike Luiken

  Universidad Carlos III de Madrid

  1444 shared

• Karen Hawkins

  Xi'an Jiaotong University

  1444 shared

• Y.-D Lin

  Northeastern University

  1444 shared

• Pietro Rossi

  University of Bologna

  1444 shared

• Jeffrey Cichocki

  Institute of Electrical and Electronics Engineers

  1444 shared

• Sophia Muirhead

  Institute of Electrical and Electronics Engineers

  1444 shared

Awards & honors

• IEEE Ocean Engineering Society’s Distinguished Technical Ach…
• IEEE Ocean Engineering Society’s Distinguished Lecture ship…
• IEEE WICE Outstanding Achievement Award (2019)
• IEEE Communication’s Society’s Stars in Computer Networking…
• Honorary doctorate, Aarhus University, Denmark