In many practical applications including remote sensing, multi-task learning, and multi-spectrum imaging, data are described as a set of matrices sharing a common column space. We consider the joint estimation of such matrices from their noisy linear measurements. We study a convex estimator regularized by a pair of matrix norms. The measurement model corresponds to block-wise sensing and the reconstruction is possible only when the total energy is well distributed over blocks. The first norm, which is the maximum-block-Frobenius norm, favors such a solution.

Particle beam microscopy (PBM) performs nanoscale imaging by pixelwise capture of scalar values representing noisy measurements of the response from secondary electrons (SEs) integrated over a dwell time. Extended to metrology, goals include estimating SE yield at each pixel and detecting differences in SE yield across pixels; obstacles include shot noise in the particle source as well as lack of knowledge of and variability in the instrument response to single SEs.

We consider the problem of active sensing and sequential beam tracking at mmWave frequencies and above. We focus on the setting of aerial communications between a quasi-stationary receiver and mobile transmitter, for example, a gateway array tracking a small agile drone, where we formulate the problem to be equivalent to actively sensing and tracking an optimal beamforming vector along the single dominant (often line-of-sight) path.

Modern computation and networking environments are struggling to store, communicate and process data in unprecedented volumes. These data, which come in new and evolving structures and formats, necessitate compression, lossless and lossy. Recent years have witnessed the emergence of new techniques, approaches, architectures and modes for data compression. This special issue is dedicated to cutting-edge research geared toward providing information theoretic insight into this space.

This paper considers information-theoretic models for integrated sensing and communication (ISAC) over multi-access channels (MAC) and device-to-device (D2D) communication. The models are general and include as special cases scenarios with and without perfect or imperfect state-information at the MAC receiver as well as causal state-information at the D2D terminals.