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Found 33 papers, 4 papers with code
Optimal Pilot Design for OTFS in Linear Time-Varying Channels
This paper investigates the positioning of the pilot symbols, as well as the power distribution between the pilot and the communication symbols in the OTFS modulation scheme.
A Generalization of the Convolution Theorem and its Connections to Non-Stationarity and the Graph Frequency Domain
In this paper, we present a novel convolution theorem which encompasses the well known convolution theorem in (graph) signal processing as well as the one related to time-varying filters.
Learning graphs and simplicial complexes from data
Graphs are widely used to represent complex information and signal domains with irregular support.
Sensor Selection using the Two-Target Cramér-Rao Bound for Angle of Arrival Estimation
Sensor selection is a useful method to help reduce data throughput, as well as computational, power, and hardware requirements, while still maintaining acceptable performance.
Deep-Learning-Aided Alternating Least Squares for Tensor CP Decomposition and Its Application to Massive MIMO Channel Estimation
To achieve accurate and low-latency channel estimation, good and fast CP decomposition algorithms are desired.
Graph Signal Processing: History, Development, Impact, and Outlook
Graph signal processing (GSP) generalizes signal processing (SP) tasks to signals living on non-Euclidean domains whose structure can be captured by a weighted graph.
Super-Resolution Harmonic Retrieval of Non-Circular Signals
This paper proposes a super-resolution harmonic retrieval method for uncorrelated strictly non-circular signals, whose covariance and pseudo-covariance present Toeplitz and Hankel structures, respectively.
Forecasting Graph Signals with Recursive MIMO Graph Filters
Forecasting time series on graphs is a fundamental problem in graph signal processing.
Blind Polynomial Regression
Fitting a polynomial to observed data is an ubiquitous task in many signal processing and machine learning tasks, such as interpolation and prediction.
Joint Ranging and Phase Offset Estimation for Multiple Drones using ADS-B Signatures
While the proposed estimator can estimate the range of multiple drones with a single receive antenna, a larger number of drones can be supported with higher accuracy by the use of multiple antennas at the receiver.
Structured Sensing Matrix Design for In-sector Compressed mmWave Channel Estimation
Fast millimeter wave (mmWave) channel estimation techniques based on compressed sensing (CS) suffer from low signal-to-noise ratio (SNR) in the channel measurements, due to the use of wide beams.
Simplicial Convolutional Filters
We study linear filters for processing signals supported on abstract topological spaces modeled as simplicial complexes, which may be interpreted as generalizations of graphs that account for nodes, edges, triangular faces etc.
Learning Time-Varying Graphs from Online Data
This work proposes an algorithmic framework to learn time-varying graphs from online data.
Simplicial Convolutional Neural Networks
Graphs can model networked data by representing them as nodes and their pairwise relationships as edges.
Self Calibration of Scalar and Vector Sensor Arrays
This joint estimation problem is referred to as self calibration.
Finite Impulse Response Filters for Simplicial Complexes
In this paper, we study linear filters to process signals defined on simplicial complexes, i. e., signals defined on nodes, edges, triangles, etc.
Online Time-Varying Topology Identification via Prediction-Correction Algorithms
Signal processing and machine learning algorithms for data supported over graphs, require the knowledge of the graph topology.
Topology-Aware Joint Graph Filter and Edge Weight Identification for Network Processes
Therefore, in this paper, we focus on the joint identification of coefficients and graph weights defining the graph filter that best models the observed input/output network data.
How Does Momentum Help Frank Wolfe?
We unveil the connections between Frank Wolfe (FW) type algorithms and the momentum in Accelerated Gradient Methods (AGM).
Submodularity in Action: From Machine Learning to Signal Processing Applications
We introduce a variety of submodular-friendly applications, and elucidate the relation of submodularity to convexity and concavity which enables efficient optimization.
Compressed-Domain Detection and Estimation for Colocated MIMO Radar
CSP MIMO radar involves two levels of data compression followed by target detection at the compressed domain.
Forecasting Multi-Dimensional Processes over Graphs
The forecasting of multi-variate time processes through graph-based techniques has recently been addressed under the graph signal processing framework.
Graphs, Convolutions, and Neural Networks: From Graph Filters to Graph Neural Networks
We also introduce GNN extensions using edge-varying and autoregressive moving average graph filters and discuss their properties.
Online Graph-Adaptive Learning with Scalability and Privacy
Moreover, new nodes can emerge over time, which can necessitate real-time evaluation of their nodal attributes.
Sampling and Reconstruction of Signals on Product Graphs
In this paper, we consider the problem of subsampling and reconstruction of signals that reside on the vertices of a product graph, such as sensor network time series, genomic signals, or product ratings in a social network.
Sparse Sampling for Inverse Problems with Tensors
We consider the problem of designing sparse sampling strategies for multidomain signals, which can be represented using tensors that admit a known multilinear decomposition.
Information Theory Signal Processing Information Theory
Convolutional Neural Network Architectures for Signals Supported on Graphs
Multinode aggregation GNNs are consistently the best performing GNN architecture.
MIMO Graph Filters for Convolutional Neural Networks
Superior performance and ease of implementation have fostered the adoption of Convolutional Neural Networks (CNNs) for a wide array of inference and reconstruction tasks.
Convolutional Neural Networks Via Node-Varying Graph Filters
Convolutional neural networks (CNNs) are being applied to an increasing number of problems and fields due to their superior performance in classification and regression tasks.
Adaptive Graph Signal Processing: Algorithms and Optimal Sampling Strategies
Numerical simulations carried out over both synthetic and real data illustrate the good performance of the proposed sampling and reconstruction strategies for (possibly distributed) adaptive learning of signals defined over graphs.
Learning Sparse Graphs Under Smoothness Prior
Given the noisy data, we show that the joint sparse graph learning and denoising problem can be simplified to designing only the sparse edge selection vector, which can be solved using convex optimization.
Autoregressive Moving Average Graph Filtering
We design a family of autoregressive moving average (ARMA) recursions, which (i) are able to approximate any desired graph frequency response, and (ii) give exact solutions for tasks such as graph signal denoising and interpolation.
Compressed Sensing for Block-Sparse Smooth Signals
We present reconstruction algorithms for smooth signals with block sparsity from their compressed measurements.
