Related papers: Sparse Vector Transmission: An Idea Whose Time Has…

Spectrum sharing backscatter communication systems are among the most prominent technologies for ultralow power and spectrum efficient communications. In this paper, we propose an underlay spectrum sharing backscatter communication system,…

As the trend towards small, safe, smart, speedy and swarm development grows, unmanned aerial vehicles (UAVs) are becoming increasingly popular for a wide range of applications. In this letter, the challenge of wideband spectrum acquisition…

Recent years have seen several new directions in the design of sparse control of cyber-physical systems (CPSs) driven by the objective of reducing communication cost. One common assumption made in these designs is that the communication…

Transformers are powerful sequence models, but require time and memory that grows quadratically with the sequence length. In this paper we introduce sparse factorizations of the attention matrix which reduce this to $O(n \sqrt{n})$. We also…

Short-packet communication (SPC) and unmanned aerial vehicles (UAVs) are anticipated to play crucial roles in the development of 5G-and-beyond wireless networks and the Internet of Things (IoT). In this paper, we propose a secure SPC…

Sparse superposition codes were originally proposed as a capacity-achieving communication scheme over the gaussian channel, whose coding matrices were made of i.i.d. gaussian entries.We extend this coding scheme to more generic ensembles of…

Small satellites are widely used today as cost effective means to perform Earth observation and other tasks that generate large amounts of high-dimensional data, such as multi-spectral imagery. These satellites typically operate in low…

Recently, wireless communication industries have begun to extend their services to machine-type communication devices as well as to user equipments. Such machine-type communication devices as meters and sensors need intermittent uplink…

The rapid development of artificial intelligence has significantly advanced semantic communications, particularly in wireless image transmission. However, most existing approaches struggle to precisely distinguish and prioritize image…

We study the problem of estimating at a central server the mean of a set of vectors distributed across several nodes (one vector per node). When the vectors are high-dimensional, the communication cost of sending entire vectors may be…

Vision transformers (ViTs) have recently received explosive popularity, but their enormous model sizes and training costs remain daunting. Conventional post-training pruning often incurs higher training budgets. In contrast, this paper aims…

Sparse coding (SC) is attracting more and more attention due to its comprehensive theoretical studies and its excellent performance in many signal processing applications. However, most existing sparse coding algorithms are nonconvex and…

The success of the compressed sensing paradigm has shown that a substantial reduction in sampling and storage complexity can be achieved in certain linear and non-adaptive estimation problems. It is therefore an advisable strategy for…

Time series analysis is vital for numerous applications, and transformers have become increasingly prominent in this domain. Leading methods customize the transformer architecture from NLP and CV, utilizing a patching technique to convert…

Vehicular ad hoc networks (VANETs) allow vehicles to exchange warning messages with each other. These specific kinds of networks help reduce hazardous traffic situations and improve safety, which are two of the main objectives in developing…

Understanding the general priniciples behind transformer models remains a complex endeavor. Experiments with probing and disentangling features using sparse autoencoders (SAE) suggest that these models might manage linear features embedded…

This paper proposes prediction-and-sensing based spectrum sharing, a new spectrum-sharing model for cognitive radio networks, with a time structure for each resource block divided into a spectrum prediction-and-sensing phase and a data…

We propose novel compression algorithms for time-varying channel state information (CSI) in wireless communications. The proposed scheme combines (lossy) vector quantisation and (lossless) compression. First, the new vector quantisation…

We propose a novel framework to learn how to communicate with intent, i.e., to transmit messages over a wireless communication channel based on the end-goal of the communication. This stays in stark contrast to classical communication…

General-purpose pretrained sentence encoders such as BERT are not ideal for real-world conversational AI applications; they are computationally heavy, slow, and expensive to train. We propose ConveRT (Conversational Representations from…