We study the information-theoretic limits of joint communication and sensing when the sensing task is modeled as the estimation of a discrete channel state fixed during the transmission of an entire codeword. This setting captures scenarios in which the time scale over which sensing happens is significantly slower than the time scale over which symbol transmission occurs. The tradeoff between communication and sensing then takes the form of a tradeoff region between the rate of reliable communication and the state detection-error exponent.

This work considers the problem of mitigating information leakage between communication and sensing in systems jointly performing both operations. Specifically, a discrete memoryless state-dependent broadcast channel model is studied in which (i) the presence of feedback enables a transmitter to convey information, while simultaneously performing channel state estimation; (ii) one of the receivers is treated as an eavesdropper whose state should be estimated but which should remain oblivious to part of the transmitted information.

Spike deconvolution is the problem of recovering the point sources from their convolution with a known point spread function, which plays a fundamental role in many sensing and imaging applications. In this paper, we investigate the local geometry of recovering the parameters of point sources—including both amplitudes and locations—by minimizing a natural nonconvex least-squares loss function measuring the observation residuals.

Storage-efficient privacy-preserving learning is crucial due to increasing amounts of sensitive user data required for modern learning tasks. We propose a framework for reducing the storage cost of user data while at the same time providing privacy guarantees, without essential loss in the utility of the data for learning. Our method comprises noise injection followed by lossy compression.

Deep neural networks have shown incredible performance for inference tasks in a variety of domains, but require significant storage space, which limits scaling and use for on-device intelligence. This paper is concerned with finding universal lossless compressed representations of deep feedforward networks with synaptic weights drawn from discrete sets, and directly performing inference without full decompression.

The multi-armed bandit (MAB) problem is one of the most well-known active learning frameworks. The aim is to select the best among a set of actions by sequentially observing rewards that come from an unknown distribution. Recently, a number of distributed bandit applications have become popular over wireless networks, where agents geographically separated from a learner collect and communicate the observed rewards. In this paper we propose a compression scheme, that compresses the rewards collected by the distributed agents.

Presents the table of contents for this issue of the publication.

Welcome to the eleventh issue of the Journal on Selected Areas in Information Theory (JSAIT), dedicated to “Deep Learning Methods for Inverse Problems”.