A comprehensive survey on spectrum sensing in cognitive radio networks: Recent advances, new challenges, and future research directions
Y Arjoune, N Kaabouch - Sensors, 2019 - mdpi.com
Cognitive radio technology has the potential to address the shortage of available radio
spectrum by enabling dynamic spectrum access. Since its introduction, researchers have …
spectrum by enabling dynamic spectrum access. Since its introduction, researchers have …
Statistical physics of inference: Thresholds and algorithms
L Zdeborová, F Krzakala - Advances in Physics, 2016 - Taylor & Francis
Many questions of fundamental interest in today's science can be formulated as inference
problems: some partial, or noisy, observations are performed over a set of variables and the …
problems: some partial, or noisy, observations are performed over a set of variables and the …
A survey on learning to hash
Nearest neighbor search is a problem of finding the data points from the database such that
the distances from them to the query point are the smallest. Learning to hash is one of the …
the distances from them to the query point are the smallest. Learning to hash is one of the …
Optimal errors and phase transitions in high-dimensional generalized linear models
Generalized linear models (GLMs) are used in high-dimensional machine learning,
statistics, communications, and signal processing. In this paper we analyze GLMs when the …
statistics, communications, and signal processing. In this paper we analyze GLMs when the …
Capacity analysis of one-bit quantized MIMO systems with transmitter channel state information
With bandwidths on the order of a gigahertz in emerging wireless systems, high-resolution
analog-to-digital convertors (ADCs) become a power consumption bottleneck. One solution …
analog-to-digital convertors (ADCs) become a power consumption bottleneck. One solution …
A farewell to the bias-variance tradeoff? an overview of the theory of overparameterized machine learning
Y Dar, V Muthukumar, RG Baraniuk - arXiv preprint arXiv:2109.02355, 2021 - arxiv.org
The rapid recent progress in machine learning (ML) has raised a number of scientific
questions that challenge the longstanding dogma of the field. One of the most important …
questions that challenge the longstanding dogma of the field. One of the most important …
A feasible method for optimization with orthogonality constraints
Minimization with orthogonality constraints (eg, X^ ⊤ X= I) and/or spherical constraints (eg,
‖ x ‖ _2= 1) has wide applications in polynomial optimization, combinatorial optimization …
‖ x ‖ _2= 1) has wide applications in polynomial optimization, combinatorial optimization …
Robust 1-bit compressive sensing via binary stable embeddings of sparse vectors
The compressive sensing (CS) framework aims to ease the burden on analog-to-digital
converters (ADCs) by reducing the sampling rate required to acquire and stably recover …
converters (ADCs) by reducing the sampling rate required to acquire and stably recover …
Robust 1-bit compressed sensing and sparse logistic regression: A convex programming approach
Y Plan, R Vershynin - IEEE Transactions on Information Theory, 2012 - ieeexplore.ieee.org
This paper develops theoretical results regarding noisy 1-bit compressed sensing and
sparse binomial regression. We demonstrate that a single convex program gives an …
sparse binomial regression. We demonstrate that a single convex program gives an …
Channel estimation in millimeter wave MIMO systems with one-bit quantization
We develop channel estimation agorithms for millimeter wave (mmWave) multiple input
multiple output (MIMO) systems with one-bit analog-to-digital converters (ADCs). Since the …
multiple output (MIMO) systems with one-bit analog-to-digital converters (ADCs). Since the …