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We consider over-the-air convex optimization on a $d$ dimensional space where coded gradients are sent over an additive Gaussian noise channel with variance $\sigma ^{2}$ . The codewords satisfy an average power constraint $P$ , resulting in the signal-to-noise ratio (SNR) of $P/\sigma ^{2}$ . We derive bounds for the convergence rates for over-the-air optimization. Our first result is a lower bound for the convergence rate showing that any code must slowdown the convergence rate by a factor of roughly $\sqrt {d/\log (1+ \mathtt {SNR})}$ .

Information theory, starting with Shannon’s groundbreaking work, has fundamentally shaped the way communication systems are designed and operated. Information theoretic principles form the underpinnings of modern communication networks. This issue explores how new advances in information theory can impact future communication systems.

In this paper, we characterize resolution limits for imaging in electromagnetic spectrum where multipath is commonly encountered, e.g., spectrum often used for wireless communication. We analyze a passive system configuration with an aperture of fixed spatial extent sampling fields backscattered from an imaging scene consisting of both line-of-sight (LOS) and non-line-of-sight (NLOS) paths. We characterize the resolution limits using the degrees of freedom (DoF) metric.

We consider a network consisting of a single source and $n$ receiver nodes that are grouped into equal-sized clusters. Each cluster corresponds to a distinct community such that nodes that belong to different communities cannot exchange information. We use dedicated cluster heads in each cluster to facilitate communication between the source and the nodes within that cluster. Inside clusters, nodes are connected to each other according to a given network topology.

With the advent of 5G and technologies such as cloud computing, Internet-of-Things (IoT), etc, future communication networks will consist of a large number of heterogeneous devices connected together. A critical aspect will be ensuring that communication is not only fast and reliable, but also resilient to malicious attack. As networks increasingly adopt zero-trust principles in their security frameworks, it is important to consider such attacks from a zero-trust perspective.

This paper considers the Gaussian multiple-access channel in the asymptotic regime where the number of users grows linearly with the code length. We propose efficient coding schemes based on random linear models with approximate message passing (AMP) decoding and derive the asymptotic error rate achieved for a given user density, user payload (in bits), and user energy. The tradeoff between energy-per-bit and achievable user density (for a fixed user payload and target error rate) is studied.