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Found 51 papers, 15 papers with code
Representation-Constrained Autoencoders and an Application to Wireless Positioning
In a number of practical applications that rely on dimensionality reduction, the dataset or measurement process provides valuable side information that can be incorporated when learning low-dimensional embeddings.
Jammer-Resilient Time Synchronization in the MIMO Uplink
JASS detects a randomized synchronization sequence based on a novel optimization problem that fits a spatial filter to the time-windowed receive signal in order to mitigate the jammer.
An Aliasing-Free Hybrid Digital-Analog Polyphonic Synthesizer
At the heart of the synthesizer is the big Fourier oscillator (BFO), a novel digital very-large scale integration (VLSI) design that utilizes additive synthesis to generate a wide variety of aliasing-free waveforms.
Wireless Channel Charting: Theory, Practice, and Applications
Channel charting is a recently proposed framework that applies dimensionality reduction to channel state information (CSI) in wireless systems with the goal of associating a pseudo-position to each mobile user in a low-dimensional space: the channel chart.
Low-Complexity Blind Parameter Estimation in Wireless Systems with Noisy Sparse Signals
Furthermore, the mean-square error (MSE) is a desirable metric to be minimized in a variety of estimation and signal recovery algorithms.
DUIDD: Deep-Unfolded Interleaved Detection and Decoding for MIMO Wireless Systems
We demonstrate the efficacy of DUIDD using NVIDIA's Sionna link-level simulator in a 5G-near multi-user MIMO-OFDM wireless system with a novel low-complexity soft-input soft-output data detector, an optimized low-density parity-check decoder, and channel vectors from a commercial ray-tracer.
Attacking and Defending Deep-Learning-Based Off-Device Wireless Positioning Systems
Localization services for wireless devices play an increasingly important role in our daily lives and a plethora of emerging services and applications already rely on precise position information.
Bit Error and Block Error Rate Training for ML-Assisted Communication
Even though machine learning (ML) techniques are being widely used in communications, the question of how to train communication systems has received surprisingly little attention.
Improving Channel Charting using a Split Triplet Loss and an Inertial Regularizer
Channel charting is an emerging technology that enables self-supervised pseudo-localization of user equipments by performing dimensionality reduction on large channel-state information (CSI) databases that are passively collected at infrastructure base stations or access points.
A Resolution-Adaptive 8 mm$^\text{2}$ 9.98 Gb/s 39.7 pJ/b 32-Antenna All-Digital Spatial Equalizer for mmWave Massive MU-MIMO in 65nm CMOS
All-digital millimeter-wave (mmWave) massive multi-user multiple-input multiple-output (MU-MIMO) receivers enable extreme data rates but require high power consumption.
Resolution-Adaptive All-Digital Spatial Equalization for mmWave Massive MU-MIMO
All-digital basestation (BS) architectures for millimeter-wave (mmWave) massive multi-user multiple-input multiple-output (MU-MIMO), which equip each radio-frequency chain with dedicated data converters, have advantages in spectral efficiency, flexibility, and baseband-processing simplicity over hybrid analog-digital solutions.
Optimality of the Discrete Fourier Transform for Beamspace Massive MU-MIMO Communication
Beamspace processing is an emerging technique to reduce baseband complexity in massive multiuser (MU) multiple-input multiple-output (MIMO) communication systems operating at millimeter-wave (mmWave) and terahertz frequencies.
WrapNet: Neural Net Inference with Ultra-Low-Precision Arithmetic
Low-precision neural networks represent both weights and activations with few bits, drastically reducing the multiplication complexity.
Distortion-Aware Linear Precoding for Massive MIMO Downlink Systems with Nonlinear Power Amplifiers
By studying the spatial characteristics of the distortion, we demonstrate that conventional linear precoding techniques steer nonlinear distortions towards the users.
Information Theory Signal Processing Information Theory
Analog vs. Digital Spatial Transforms: A Throughput, Power, and Area Comparison
Spatial linear transforms that process multiple parallel analog signals to simplify downstream signal processing find widespread use in multi-antenna communication systems, machine learning inference, data compression, audio and ultrasound applications, among many others.
High-Bandwidth Spatial Equalization for mmWave Massive MU-MIMO with Processing-In-Memory
All-digital basestation (BS) architectures enable superior spectral efficiency compared to hybrid solutions in massive multi-user MIMO systems.
CSI-Based Multi-Antenna and Multi-Point Indoor Positioning Using Probability Fusion
Channel state information (CSI)-based fingerprinting via neural networks (NNs) is a promising approach to enable accurate indoor and outdoor positioning of user equipments (UEs), even under challenging propagation conditions.
Algorithm and VLSI Design for 1-bit Data Detection in Massive MIMO-OFDM
Since the system performance heavily depends on the quality of channel estimates, we also develop a nonlinear 1-bit channel estimation algorithm that builds upon the proposed data detection algorithm.
Optimal Data Detection and Signal Estimation in Systems with Input Noise
Practical systems often suffer from hardware impairments that already appear during signal generation.
WrapNet: Neural Net Inference with Ultra-Low-Resolution Arithmetic
Low-resolution neural networks represent both weights and activations with few bits, drastically reducing the multiplication complexity.
Identifying Unused RF Channels Using Least Matching Pursuit
Cognitive radio aims at identifying unused radio-frequency (RF) bands with the goal of re-using them opportunistically for other services.
Headless Horseman: Adversarial Attacks on Transfer Learning Models
We first demonstrate successful transfer attacks against a victim network using \textit{only} its feature extractor.
Certified Defenses for Adversarial Patches
Adversarial patch attacks are among one of the most practical threat models against real-world computer vision systems.
MSE-Optimal Neural Network Initialization via Layer Fusion
To address this issue, a variety of methods that rely on random parameter initialization or knowledge distillation have been proposed in the past.
Siamese Neural Networks for Wireless Positioning and Channel Charting
In this paper, we propose a unified architecture based on Siamese networks that can be used for supervised UE positioning and unsupervised channel charting.
Improving Channel Charting with Representation-Constrained Autoencoders
Channel charting (CC) has been proposed recently to enable logical positioning of user equipments (UEs) in the neighborhood of a multi-antenna base-station solely from channel-state information (CSI).
Adversarially robust transfer learning
By training classifiers on top of these feature extractors, we produce new models that inherit the robustness of their parent networks.
Transferable Clean-Label Poisoning Attacks on Deep Neural Nets
Clean-label poisoning attacks inject innocuous looking (and "correctly" labeled) poison images into training data, causing a model to misclassify a targeted image after being trained on this data.
Adversarial Training for Free!
Adversarial training, in which a network is trained on adversarial examples, is one of the few defenses against adversarial attacks that withstands strong attacks.
Are adversarial examples inevitable?
Using experiments, we explore the implications of theoretical guarantees for real-world problems and discuss how factors such as dimensionality and image complexity limit a classifier's robustness against adversarial examples.
Channel Charting: Locating Users within the Radio Environment using Channel State Information
We propose channel charting (CC), a novel framework in which a multi-antenna network element learns a chart of the radio geometry in its surrounding area.
Linear Spectral Estimators and an Application to Phase Retrieval
Phase retrieval refers to the problem of recovering real- or complex-valued vectors from magnitude measurements.
An Estimation and Analysis Framework for the Rasch Model
The Rasch model is widely used for item response analysis in applications ranging from recommender systems to psychology, education, and finance.
Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks
The proposed attacks use "clean-labels"; they don't require the attacker to have any control over the labeling of training data.
Linearized Binary Regression
We showcase the efficacy of our methods and results for a number of synthetic and real-world datasets, which demonstrates that linearized binary regression finds potential use in a variety of inference, estimation, signal processing, and machine learning applications that deal with binary-valued observations or measurements.
Visualizing the Loss Landscape of Neural Nets
Neural network training relies on our ability to find "good" minimizers of highly non-convex loss functions.
Convex Phase Retrieval without Lifting via PhaseMax
Semidefinite relaxation methods transform a variety of non-convex optimization problems into convex problems, but square the number of variables.
Training Quantized Nets: A Deeper Understanding
Currently, deep neural networks are deployed on low-power portable devices by first training a full-precision model using powerful hardware, and then deriving a corresponding low-precision model for efficient inference on such systems.
Adaptive Relaxed ADMM: Convergence Theory and Practical Implementation
Relaxed ADMM is a generalization of ADMM that often achieves better performance, but its efficiency depends strongly on algorithm parameters that must be chosen by an expert user.
An Empirical Study of ADMM for Nonconvex Problems
The alternating direction method of multipliers (ADMM) is a common optimization tool for solving constrained and non-differentiable problems.
Biconvex Relaxation for Semidefinite Programming in Computer Vision
We propose a general framework to approximately solve large-scale semidefinite problems (SDPs) at low complexity.
Estimating Sparse Signals with Smooth Support via Convex Programming and Block Sparsity
We demonstrate the efficacy of our regularizers on a variety of imaging tasks including compressive image recovery, image restoration, and robust PCA.
Video Compressive Sensing for Spatial Multiplexing Cameras using Motion-Flow Models
In this paper, we propose the CS multi-scale video (CS-MUVI) sensing and recovery framework for high-quality video acquisition and recovery using SMCs.
FASTA: A Generalized Implementation of Forward-Backward Splitting
This is a user manual for the software package FASTA.
Mathematical Software Numerical Analysis Numerical Analysis
SPRITE: A Response Model For Multiple Choice Testing
In this work, we propose a novel methodology for unordered categorical IRT that we call SPRITE (short for stochastic polytomous response item model) that: (i) analyzes both ordered and unordered categories, (ii) offers interpretable outputs, and (iii) provides improved data fitting compared to existing models.
Quantized Matrix Completion for Personalized Learning
The recently proposed SPARse Factor Analysis (SPARFA) framework for personalized learning performs factor analysis on ordinal or binary-valued (e. g., correct/incorrect) graded learner responses to questions.
Tag-Aware Ordinal Sparse Factor Analysis for Learning and Content Analytics
SPARse Factor Analysis (SPARFA) is a novel framework for machine learning-based learning analytics, which estimates a learner's knowledge of the concepts underlying a domain, and content analytics, which estimates the relationships among a collection of questions and those concepts.
A Field Guide to Forward-Backward Splitting with a FASTA Implementation
Non-differentiable and constrained optimization play a key role in machine learning, signal and image processing, communications, and beyond.
Numerical Analysis G.1.6
Time-varying Learning and Content Analytics via Sparse Factor Analysis
We propose SPARFA-Trace, a new machine learning-based framework for time-varying learning and content analytics for education applications.
Joint Topic Modeling and Factor Analysis of Textual Information and Graded Response Data
In order to better interpret the estimated latent concepts, SPARFA relies on a post-processing step that utilizes user-defined tags (e. g., topics or keywords) available for each question.
Sparse Factor Analysis for Learning and Content Analytics
We estimate these factors given the graded responses to a collection of questions.
