Air Force Office of Air Force Research Laboratory/Air Force Office of Scientific Research University Center of Excellence: Agile Waveform Design for Communication Networks in Contested Environments Research University Center of Excellence
Developing AI-informed communication and networking protocols
Rhodes iiD researchers are working with colleagues across the nation to ensure that future communication protocols used by the United States Air Force are suitable for handling the most data-heavy tasks imaginable, such as flying UAVs, and secure from adversarial attack.
The project is led by Robert Calderbank, the Charles S. Sydnor Distinguished Professor of Computer Science, Electrical and Computer Engineering, and Mathematics, and director of the Rhodes Information Initiative at Duke, and Vahid Tarokh, the Rhodes Family Professor of Electrical and Computer Engineering.
The new center also draws in research expertise from Virginia Tech, Princeton University, Carnegie Mellon University, Colorado State University, and Arizona State University.
The project will deepen existing collaborations between the universities involved and the Air Force Research Laboratory (AFRL). By tackling this new challenge, the researchers will increase the capabilities, knowledge, skills, and expertise of the AFRL workforce, while giving its staff opportunities to work with a large pipeline of talented students through programs like Data+ and Code+, both ten-week summer research experiences that pair mixed teams of Duke undergraduate and graduate students with real-life data sets and problems from partnering companies.
November 16, 2020 Meeting
Contact: Yi Feng, Ph.D.
Title: Power Amplifier Predistortion via Reversible Deep Neural Networks
Abstract: Hardware limitations may be key issues in the efficient communications in contested environments. In particular, power amplifiers (PA) is a key element that must be considered. In practice, there may always exist inherent non-linearities in power amplifiers causing signal constellation compression and bandwidth growth. In this work, we design a digital pre-distorter to compensate these non-linearities. Inspired by the idea of Normalizing Flows, we propose a reversible Deep Neural Network (DNN) based architecture and construct digital pre-distorters for mitigation of the non-linearities. Our approach gives significant linearization improvements over state of the art. Simulations are presented demonstrating these significant improvements.
November 30, 2020 Meeting
Speaker: Yuejie Chi, CMU
Title: Accelerating Ill-Conditioned Low-Rank Matrix Estimation via Scaled Gradient Descent
Abstract: Low-rank matrix estimation is a canonical problem that finds numerous applications in signal processing, machine learning and imaging science. A popular approach in practice is to factorize the matrix into two compact low-rank factors, and then optimize these factors directly via simple iterative methods such as gradient descent and alternating minimization. Despite non-convexity, recent literatures have shown that these simple heuristics in fact achieve linear convergence when initialized properly for a growing number of problems of interest. However, upon closer examination, existing approaches can still be computationally expensive especially for ill-conditioned matrices: the convergence rate of gradient descent depends linearly on the condition number of the low-rank matrix, while the per-iteration cost of alternating minimization is often prohibitive for large matrices.
The goal of this paper is to set forth a competitive algorithmic approach dubbed Scaled Gradient Descent (ScaledGD) which can be viewed as pre-conditioned or diagonally-scaled gradient descent, where the pre-conditioners are adaptive and iteration-varying with a minimal computational overhead. With tailored variants for low-rank matrix sensing, robust principal component analysis and matrix completion, we theoretically show that ScaledGD achieves the best of both worlds: it converges linearly at a rate independent of the condition number of the low-rank matrix similar as alternating minimization, while maintaining the low per-iteration cost of gradient descent. To the best of our knowledge, ScaledGD is the first algorithm that provably has such properties over a wide range of low-rank matrix estimation tasks.
January 11, 2021 Meeting
Title: Bounds on Bearing, Symbol, and Channel Estimation under Model Misspecification
Abstract: The constrained Cramér-Rao bound (CRB) has been used successfully to study parameter estimation in flat fading scenarios, and to establish the value of side information such as known waveform properties (e.g. constant modulus) and known training symbols. There are classes of communication links, however, that may be subject to highly dynamic changes, and this could cause the assumed data model to be an inaccurate model of the channel. Therefore, the constrained misspecified CRB is considered to explore the impact of model mismatch for such communication links. Specifically, quantifying the loss in estimation performance when one assumes the channel is stationary when it is not is of interest. As we explore the application of machine/deep learning to dynamic channels, measures such as the constrained MCRB may help to lend insights into convergence rates, benefits of transfer learning, and the level of fidelity/complexity required to achieve desired performance.
This is joint work with Akshay S. Bondre and Touseef Ali.
February 8, 2021 Meeting
Title: Robust Multi-Agent AI for Contested Environments
Abstract: Many Air Force problems, such as protecting communications networks against adversaries in contested environments can be formulated as games between adversaries and defenders. The challenge is that these games, their states and actions are not fully known/observable in practice and need to be learned in real-time based online observations.
In this talk, we propose several frameworks to address these challenging problems. We propose robust learning and optimization frameworks to solve the decision-making problems confronted by real-time, non-stationary, and incomplete data (potential missing data). Furthermore, we employ neural networks to address more complex settings and propose deep reinforcement learning to learn the system's variables, states, and objectives and propose practical solutions.
This is joint work with Vahid Tarokh.
February 22, 2021 Meeting
Speaker: Lingjia Liu, Virginia Tech
Title: Learning with Knowledge of Structure: A Neural Network-Based Approach for MIMO-OFDM Detection
Abstract: We explore neural network-based strategies for performing symbol detection in a MIMO-OFDM system. Building on a reservoir computing (RC)-based approach towards symbol detection, we introduce a symmetric and decomposed binary decision neural network to take advantage of the structure knowledge inherent in the MIMO-OFDM system. To be specific, the binary decision neural network is added in the frequency domain utilizing the knowledge of the constellation. We show that the introduced symmetric neural network can decompose the original M-ary detection problem into a series of binary classification tasks, thus significantly reducing the neural network detector complexity while offering good generalization performance with limited training overhead. Numerical evaluations demonstrate that the introduced hybrid RC-binary decision detection framework performs close to maximum likelihood model-based symbol detection methods in terms of symbol error rate in the low SNR regime with imperfect channel state information (CSI).
March 8, 2021 Meeting
Title: Wireless System Design using Optimization and Machine Learning
Abstract: Design and analysis of communication systems have traditionally relied on mathematical and statistical channel models that describe how a signal is corrupted during transmission. In particular, communication techniques such as modulation, coding and detection that mitigate performance degradation due to channel impairments are based on such channel models and, in some cases, instantaneous channel state information about the model. However, there are propagation environments where this approach does not work well because the underlying physical channel is too complicated, poorly understood, or rapidly time-varying. In these scenarios we propose completely new approaches to detection in the communication received based on optimization and machine learning (ML). In this approach, the detection algorithm utilizes tools from optimization and ML. We present results for three communication design problems where the optimization and ML approaches results in better performance than current state-of-the-art techniques: Blind Massive MIMO detection, signal detection without accurate channel state information, and signal detection without a mathematical channel model. Broader application of optimization and ML to communication system design in general and to millimeter wave communication systems in particular is also discussed.
March 22, 2021 Meeting
Speaker: Robert Calderbank, Duke University
Title: 6G Wireless – Illuminating New Directions in Waveform Design
Abstract: The world of wireless communications is changing rapidly, and I will look back at GSM, CDMA, and OFDM, and describe how these technologies developed in response to demanding use cases. I will then try to look forward at use cases motivating 6G wireless, such as drones, explore what might be possible with OFDM, and what might be more difficult. This will motivate a discussion of OTFS (Orthogonal Time Frequency Space), a physical layer technology that can be architected as an OFDM overlay.
April 5, 2021 Meeting
Speaker: Jeff Reed, Virginia Tech
Title: 5G Standardization and Satellites
Abstract: The 3GPP organization which standardizes cellular systems such as 3G, 4G, and 5G is currently looking at extending 5G’s reach to Non-Terrestrial Communications (NTC). While this work is proceeding with study groups, there are many challenges in extending the 5G waveform and the overall network architecture. This presentation will discuss the technical issues faced by 3GPP to standardize NTC, the timetable for standardization, and the anticipated interoperability issues and network architectures.