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Found 77 papers, 33 papers with code
Deep Variational Sequential Monte Carlo for High-Dimensional Observations
Sequential Monte Carlo (SMC), or particle filtering, is widely used in nonlinear state-space systems, but its performance often suffers from poorly approximated proposal and state-transition distributions.
Remote Inference over Dynamic Links via Adaptive Rate Deep Task-Oriented Vector Quantization
While deep learning facilitates joint design of the compression mapping along with encoding and inference rules, existing learned compression mechanisms are static, and struggle in adapting their resolution to changes in channel conditions and to dynamic links.
Deep Unfolding-Empowered MmWave Massive MIMO Joint Communications and Sensing
We first derive closed-form expressions for the gradients of the communications sum rate and sensing beampattern error with respect to the analog and digital precoders.
AI-Aided Kalman Filters
We review both generic and dedicated DNN architectures suitable for state estimation, and provide a systematic presentation of techniques for fusing AI tools with KFs and for leveraging partial SS modeling and data, categorizing design approaches into task-oriented and SS model-oriented.
Model-Based Machine Learning for Max-Min Fairness Beamforming Design in JCAS Systems
Joint communications and sensing (JCAS) is expected to be a crucial technology for future wireless systems.
Learning Task-Based Trainable Neuromorphic ADCs via Power-Aware Distillation
In this work, we study task-based acquisition for a generic classification task using memristive ADCs.
Learning Flock: Enhancing Sets of Particles for Multi~Sub-State Particle Filtering with Neural Augmentation
We experimentally show the improvements in performance, robustness, and latency of LF augmentation for radar multi-target tracking, as well its ability to mitigate the effect of a mismatched observation modelling.
Optimization of Iterative Blind Detection based on Expectation Maximization and Belief Propagation
We study iterative blind symbol detection for block-fading linear inter-symbol interference channels.
Rapid and Power-Aware Learned Optimization for Modular Receive Beamforming
In this work, we propose a power-oriented optimization algorithm for beamforming in uplink modular hybrid MIMO systems, which learns from data to operate rapidly.
Beam Focusing for Near-Field Multi-User Localization
The simulation results show that near-field localization accuracy based on a hybrid array or DMA can achieve performance close to that of fully digital arrays at a lower cost, and DMAs can attain better performance than hybrid solutions with the same aperture.
A Deep-NN Beamforming Approach for Dual Function Radar-Communication THz UAV
In this paper, we consider a scenario with one UAV equipped with a ULA, which sends combined information and sensing signals to communicate with multiple GBS and, at the same time, senses potential targets placed within an interested area on the ground.
Stragglers-Aware Low-Latency Synchronous Federated Learning via Layer-Wise Model Updates
Synchronous federated learning (FL) is a popular paradigm for collaborative edge learning.
Blind Channel Estimation and Joint Symbol Detection with Data-Driven Factor Graphs
We investigate the application of the factor graph framework for blind joint channel estimation and symbol detection on time-variant linear inter-symbol interference channels.
Wideband THz Multi-User Downlink Communications with Leaky Wave Antennas
Extremely massive multiple-input multiple-output (MIMO) antennas can be costly and power inefficient for wideband THz communications.
GSP-KalmanNet: Tracking Graph Signals via Neural-Aided Kalman Filtering
Dynamic systems of graph signals are encountered in various applications, including social networks, power grids, and transportation.
Limited Communications Distributed Optimization via Deep Unfolded Distributed ADMM
In this work we propose unfolded D-ADMM, which follows the emerging deep unfolding methodology to enable D-ADMM to operate reliably with a predefined and small number of messages exchanged by each agent.
Outlier-Insensitive Kalman Filtering: Theory and Applications
State estimation of dynamical systems from noisy observations is a fundamental task in many applications.
Adaptive KalmanNet: Data-Driven Kalman Filter with Fast Adaptation
Combining the classical Kalman filter (KF) with a deep neural network (DNN) enables tracking in partially known state space (SS) models.
Deep Learning-Aided Subspace-Based DOA Recovery for Sparse Arrays
By doing so, we learn to cope with coherent sources and miscalibrated sparse arrays, while preserving the interpretability and the suitability of model-based subspace DoA estimators.
Bayesian KalmanNet: Quantifying Uncertainty in Deep Learning Augmented Kalman Filter
This architecture employs sampling techniques to predict error covariance reliably without requiring additional domain knowledge, while retaining KalmanNet's ability to accurately track in partially known dynamics.
NUV-DoA: NUV Prior-based Bayesian Sparse Reconstruction with Spatial Filtering for Super-Resolution DoA Estimation
Achieving high-resolution Direction of Arrival (DoA) recovery typically requires high Signal to Noise Ratio (SNR) and a sufficiently large number of snapshots.
Model-Based Deep Learning
The methodologies that lie in the middle ground of this spectrum, thus integrating model-based signal processing with deep learning, are referred to as model-based deep learning, and are the focus here.
SubspaceNet: Deep Learning-Aided Subspace Methods for DoA Estimation
Direction of arrival (DoA) estimation is a fundamental task in array processing.
Latent-KalmanNet: Learned Kalman Filtering for Tracking from High-Dimensional Signals
In this work, we study tracking from high-dimensional measurements under complex settings using a hybrid model-based/data-driven approach.
AI-Empowered Hybrid MIMO Beamforming
We discuss the architectural constraints and characterize the core challenges associated with hybrid beamforming optimization.
Learn to Rapidly and Robustly Optimize Hybrid Precoding
To cope with noisy CSI, we learn to optimize the minimal achievable sum-rate among all tolerable errors, proposing a robust hybrid precoding based on the projected conceptual mirror prox minimax optimizer.
Robust Task-Specific Beamforming with Low-Resolution ADCs for Power-Efficient Hybrid MIMO Receivers
In this work, we propose a power-efficient hybrid MIMO receiver with low-quantization rate ADCs, by jointly optimizing the analog and digital processing in a hardware-oriented manner using task-specific quantization techniques.
Integrated Sensing and Communications with Reconfigurable Intelligent Surfaces
Integrated sensing and communications (ISAC) are envisioned to be an integral part of future wireless networks, especially when operating at the millimeter-wave (mmWave) and terahertz (THz) frequency bands.
Near-field Localization with Dynamic Metasurface Antennas
We use a direct positioning estimation method based on curvature-of-arrival of the impinging wavefront to obtain the user location, and characterize the effects of DMA tuning on the estimation accuracy.
HKF: Hierarchical Kalman Filtering with Online Learned Evolution Priors for Adaptive ECG Denoising
This paper introduces HKF, a hierarchical and adaptive Kalman filter, which uses a proprietary state space model to effectively capture both intra- and inter-heartbeat dynamics for ECG signal denoising.
LQGNet: Hybrid Model-Based and Data-Driven Linear Quadratic Stochastic Control
The linear quadratic Gaussian (LQG) is a widely-used setting, where the system dynamics is represented as a linear Gaussian statespace (SS) model, and the objective function is quadratic.
Joint Microstrip Selection and Beamforming Design for MmWave Systems with Dynamic Metasurface Antennas
Dynamic metasurface antennas (DMAs) provide a new paradigm to realize large-scale antenna arrays for future wireless systems.
Neural Augmented Kalman Filtering with Bollinger Bands for Pairs Trading
In this work, we propose KalmenNet-aided Bollinger bands Pairs Trading (KBPT), a deep learning aided policy that augments the operation of KF-aided BB trading.
Split-KalmanNet: A Robust Model-Based Deep Learning Approach for SLAM
The proposed split structure in the computation of the Kalman gain allows to compensate for state and measurement model mismatch effects independently.
Outlier-Insensitive Kalman Filtering Using NUV Priors
The former was previously proposed for the task of smoothing with outliers and was adapted here to filtering, while both EM and AM obtained the same performance and outperformed the other algorithms, the AM approach is less complex and thus requires 40 percentage less run-time.
Joint Privacy Enhancement and Quantization in Federated Learning
The distributed operation of FL gives rise to challenges that are not encountered in centralized machine learning, including the need to preserve the privacy of the local datasets, and the communication load due to the repeated exchange of updated models.
Collaborative Inference for AI-Empowered IoT Devices
Artificial intelligence (AI) technologies, and particularly deep learning systems, are traditionally the domain of large-scale cloud servers, which have access to high computational and energy resources.
Deep-Learning-Aided Distributed Clock Synchronization for Wireless Networks
The proliferation of wireless communications networks over the past decades, combined with the scarcity of the wireless spectrum, have motivated a significant effort towards increasing the throughput of wireless networks.
Discriminative and Generative Learning for Linear Estimation of Random Signals [Lecture Notes]
While machine learning systems often lack the interpretability of traditional signal processing methods, we focus on a simple setting where one can interpret and compare the approaches in a tractable manner that is accessible and relevant to signal processing readers.
Decentralized Low-Latency Collaborative Inference via Ensembles on the Edge
The success of deep neural networks (DNNs) is heavily dependent on computational resources.
MICAL: Mutual Information-Based CNN-Aided Learned Factor
Since the soft estimates obtained as the combined features from the neural MI estimator and the CNN do not capture the temporal correlation between different EEG blocks, we use them not as estimates of the seizure state, but to compute the function nodes of a factor graph.
Model-Based Deep Learning: On the Intersection of Deep Learning and Optimization
Decision making algorithms are used in a multitude of different applications.
6G Wireless Communications: From Far-field Beam Steering to Near-field Beam Focusing
As a consequence, it is expected that some portion of future 6G wireless communications may take place in the radiating near-field (Fresnel) region, in addition to the far-field operation as in current wireless technologies.
CNN-Aided Factor Graphs with Estimated Mutual Information Features for Seizure Detection
We then use a 1D-CNN to extract extra features from the EEG signals and use both features to estimate the probability of a seizure event.~Finally, learned factor graphs are employed to capture the temporal correlation in the signal.
Jointly Learned Symbol Detection and Signal Reflection in RIS-Aided Multi-user MIMO Systems
Reconfigurable Intelligent Surfaces (RISs) are regarded as a key technology for future wireless communications, enabling programmable radio propagation environments.
Channel Estimation with Simultaneous Reflecting and Sensing Reconfigurable Intelligent Metasurfaces
As an indicative application of HRISs, we formulate and solve the individual channels identification problem for the uplink of multi-user HRIS-empowered systems.
Deep Task-Based Analog-to-Digital Conversion
In this work we design task-oriented ADCs which learn from data how to map an analog signal into a digital representation such that the system task can be efficiently carried out.
Task-Based Graph Signal Compression
The common framework for graph signal compression is based on sampling, resulting in a set of continuous-amplitude samples, which in turn have to be quantized into a finite bit representation.
Unsupervised Learned Kalman Filtering
In this paper we adapt KalmanNet, which is a recently pro-posed deep neural network (DNN)-aided system whose architecture follows the operation of the model-based Kalman filter (KF), to learn its mapping in an unsupervised manner, i. e., without requiring ground-truth states.
Uncertainty in Data-Driven Kalman Filtering for Partially Known State-Space Models
Providing a metric of uncertainty alongside a state estimate is often crucial when tracking a dynamical system.
RTSNet: Learning to Smooth in Partially Known State-Space Models (Preprint)
The smoothing task is core to many signal processing applications.
Near-Field Wireless Power Transfer with Dynamic Metasurface Antennas
Radio frequency wireless power transfer (WPT) enables charging low-power mobile devices without relying on wired infrastructure.
DA-MUSIC: Data-Driven DoA Estimation via Deep Augmented MUSIC Algorithm
A popular multi-signal DoA estimation method is the multiple signal classification (MUSIC) algorithm, which enables high-performance super-resolution DoA recovery while being highly applicable in practice.
Near-field Wireless Power Transfer for 6G Internet-of-Everything Mobile Networks: Opportunities and Challenges
Radiating wireless power transfer (WPT) brings forth the possibility to cost-efficiently charge wireless devices without requiring a wiring infrastructure.
Efficient Epileptic Seizure Detection Using CNN-Aided Factor Graphs
We propose a computationally efficient algorithm for seizure detection.
KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamics
State estimation of dynamical systems in real-time is a fundamental task in signal processing.
FRaC: FMCW-Based Joint Radar-Communications System via Index Modulation
The proposed FMCW-based radar-communications system (FRaC) operates at reduced cost and complexity by transmitting with a reduced number of radio frequency modules, combined with narrowband FMCW signalling.
Beam Focusing for Near-Field Multi-User MIMO Communications
As the ability to achieve beam focusing is dictated by the transmit antenna, we study near-field signaling considering different antenna structures, including fully-digital architectures, hybrid phase shifter-based precoders, and the emerging dynamic metasurface antenna (DMA) architecture for massive MIMO arrays.
Federated Learning: A Signal Processing Perspective
Learning in a federated manner differs from conventional centralized machine learning, and poses several core unique challenges and requirements, which are closely related to classical problems studied in the areas of signal processing and communications.
LoRD-Net: Unfolded Deep Detection Network with Low-Resolution Receivers
The need to recover high-dimensional signals from their noisy low-resolution quantized measurements is widely encountered in communications and sensing.
Model-Based Machine Learning for Communications
We present an introduction to model-based machine learning for communication systems.
Model-Based Deep Learning
We are interested in hybrid techniques that combine principled mathematical models with data-driven systems to benefit from the advantages of both approaches.
FedRec: Federated Learning of Universal Receivers over Fading Channels
Wireless communications is often subject to channel fading.
Multi-Level Group Testing with Application to One-Shot Pooled COVID-19 Tests
A key requirement in containing contagious diseases, such as the Coronavirus disease 2019 (COVID-19) pandemic, is the ability to efficiently carry out mass diagnosis over large populations.
BiLiMO: Bit-Limited MIMO Radar via Task-Based Quantization
One of the reasons for this difficulty stems from the increased cost and power consumption required by analog-to-digital convertors (ADCs) in acquiring the multiple waveforms at the radar receiver.
Over-the-Air Federated Learning from Heterogeneous Data
Our analysis reveals the ability of COTAF to achieve a convergence rate similar to that achievable over error-free channels.
eSampling: Energy Harvesting ADCs
We analyze the tradeoff between the ability to recover the sampled signal and the energy harvested, and provide guidelines for setting the sampling rate in the light of accuracy and energy constraints.
Dynamic Metasurface Antennas for 6G Extreme Massive MIMO Communications
Next generation wireless base stations and access points will transmit and receive using extremely massive numbers of antennas.
UVeQFed: Universal Vector Quantization for Federated Learning
We show that combining universal vector quantization methods with FL yields a decentralized training system in which the compression of the trained models induces only a minimum distortion.
Learned Factor Graphs for Inference from Stationary Time Sequences
Learned factor graph can be realized using compact neural networks that are trainable using small training sets, or alternatively, be used to improve upon existing deep inference systems.
Spatial Modulation for Joint Radar-Communications Systems: Design, Analysis, and Hardware Prototype
For the radar subsystem, our experiments show that the spatial agility induced by the GSM transmission improves the angular resolution and reduces the sidelobe level in the transmit beam pattern compared to using fixed antenna allocations.
Data-Driven Symbol Detection via Model-Based Machine Learning
The design of symbol detectors in digital communication systems has traditionally relied on statistical channel models that describe the relation between the transmitted symbols and the observed signal at the receiver.
DeepSIC: Deep Soft Interference Cancellation for Multiuser MIMO Detection
In multiuser multiple-input multiple-output (MIMO) setups, where multiple symbols are simultaneously transmitted, accurate symbol detection is challenging.
Data-Driven Factor Graphs for Deep Symbol Detection
In particular, we propose to use machine learning (ML) tools to learn the factor graph, instead of the overall system task, which in turn is used for inference by message passing over the learned graph.
Deep Task-Based Quantization
In this work we design data-driven task-oriented quantization systems with scalar ADCs, which determine how to map an analog signal into its digital representation using deep learning tools.
Deep Neural Network Symbol Detection for Millimeter Wave Communications
This paper proposes to use a deep neural network (DNN)-based symbol detector for mmWave systems such that CSI acquisition can be bypassed.
ViterbiNet: A Deep Learning Based Viterbi Algorithm for Symbol Detection
Our numerical evaluations demonstrate that the performance of ViterbiNet, which is ignorant of the CSI, approaches that of the CSI-based Viterbi algorithm, and is capable of tracking time-varying channels without needing instantaneous CSI or additional training data.
