Â鶹´«Ã½AV

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.

This paper considers a multi-source updating system in which a transmitter node powered by energy harvesting (EH) sends status updates about multiple sources of information to a destination, where the freshness of status updates is measured in terms of Age of Information (AoI). The status updates of each source and harvested energy packets are assumed to arrive at the transmitter according to independent Poisson processes, and the service time of each status update is assumed to be exponentially distributed.

We study a basic joint communication and sensing setup from an information theoretic perspective. A transmitter sends an encoded message through a pair of noisy channels connected to a receiver and a sensor. The receiver is interested in decoding the message from its noisy observation. The sensor has access to the message as side information, and instead is interested in estimating an unknown yet fixed binary state of its channel.

This paper considers a single-source single-destination half-duplex $n$ -relay network with arbitrary topology, where the source communicates with the destination through a direct link and with the help of $n$ half-duplex relays. The focus is on the linear deterministic approximation of the Gaussian noise network model.

In this paper, we study a distributed learning problem constrained by constant communication bits. Specifically, we consider the distributed hypothesis testing (DHT) problem where two distributed nodes are constrained to transmit a constant number of bits to a central decoder. In such settings, we show that in order to achieve the optimal error exponents, it suffices to consider the empirical distributions of observed data sequences and encode them to the transmission bits.

This paper studies the capacity scaling of non-coherent Single-Input Multiple-Output (SIMO) independent and identically distributed (i.i.d.) Rayleigh block fading channels versus bandwidth ( $B$ ), number of receive antennas ( $N$ ) and coherence block length ( $L$ ). In non-coherent channels (without Channel State Information–CSI) capacity scales as $\Theta (\min (B,\sqrt {NL},N))$ . This is achievable using Pilot-Assisted signaling. Energy Modulation signaling rate scales as $\Theta (\min (B,\sqrt {N}))$ .

Understanding the fundamental limits of technologies enabling future wireless communication systems is essential for their efficient state-of-the-art design. A prominent technology of major interest in this framework is non-orthogonal multiple access (NOMA). In this paper, we derive an explicit rigorous closed-form analytical expression for the optimum spectral efficiency, in the large-system limit, of regular sparse (code-domain) NOMA, along with a closed-form formulation for the limiting spectral density of the underlying input covariance matrix.

Most DNA sequencing technologies are based on the shotgun paradigm: many short reads are obtained from random unknown locations in the DNA sequence. A fundamental question, in Motahari et al., (2013), is what read length and coverage depth (i.e., the total number of reads) are needed to guarantee reliable sequence reconstruction. Motivated by DNA-based storage, we study the coded version of this problem; i.e., the scenario where the DNA molecule being sequenced is a codeword from a predefined codebook.