A comprehensive survey on spectrum sensing in cognitive radio networks: Recent advances, new challenges, and future research directions
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
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
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
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 …