Related papers: A Graph Model for Opportunistic Network Coding
In this paper, we aim to identify the strategies that can maximize and monotonically increase the density of the coding opportunities in instantly decodable network coding (IDNC).Using the well-known graph representation of IDNC, first…
In this paper, we propose an opportunistic network coding (ONC) scheme in cellular relay networks, which operates depending on whether the relay decodes source messages successfully or not. A fully distributed method is presented to…
This paper addresses the problem of reducing the delivery time of data messages to cellular users using instantly decodable network coding (IDNC) with physical-layer rate awareness. While most of the existing literature on IDNC does not…
Our primary goal in this paper is to traverse the performance gap between two linear network coding schemes: random linear network coding (RLNC) and instantly decodable network coding (IDNC) in terms of throughput and decoding delay. We…
In this paper, we investigate the problem of minimizing the frame completion delay for Instantly Decodable Network Coding (IDNC) in relay-assisted wireless multicast networks. We first propose a packet recovery algorithm in the single relay…
In this paper, we study a real-time scalable video broadcast over wireless networks in instantly decodable network coded (IDNC) systems. Such real-time scalable video has a hard deadline and imposes a decoding order on the video layers.We…
We address the problem of optimizing the throughput of network coded traffic in mobile networks operating in challenging environments where connectivity is intermittent and locally available memory space is limited. Random linear network…
The current internet architecture is inefficient in fulfilling the demands of newly emerging internet applications. To address this issue, several over-the-top (OTT) application-level solutions have been employed, making the overall…
Network coding is known to improve the throughput and the resilience to losses in most network scenarios. In a practical network scenario, however, the accurate modeling of the traffic is often too complex and/or infeasible. The goal is…
Consider a scenario of broadcasting a common content to a group of cooperating mobile devices that are within proximity of each other. Devices in this group may receive partial content from the source due to packet losses over wireless…
In this paper, we consider the problem of minimizing the completion delay for instantly decodable network coding (IDNC), in wireless multicast and broadcast scenarios. We are interested in this class of network coding due to its numerous…
As massive graphs become more prevalent, there is a rapidly growing need for scalable algorithms that solve classical graph problems, such as maximum matching and minimum vertex cover, on large datasets. For massive inputs, several…
In this paper, we study the broadcast decoding delay performance of generalized instantly decodable network coding (G-IDNC) in the lossy feedback scenario. The problem is formulated as a maximum weight clique problem over the G-IDNC graph…
Random Linear Network Coding (RLNC) has emerged as a powerful tool for robust high-throughput multicast. Projection analysis - a recently introduced technique - shows that the distributed packetized RLNC protocol achieves (order) optimal…
Coding schemes with extremely low computational complexity are required for particular applications, such as wireless body area networks, in which case both very high data accuracy and very low power-consumption are required features. In…
This paper tackles the problem of transmitting a common content to a number of cellular users by means of instantly decodable network coding (IDNC) with the help of intermittently connected D2D links. Of particular interest are broadcasting…
When testing data and training data come from different distributions, deep neural networks (DNNs) will face significant safety risks in practical applications. Therefore, out-of-distribution (OOD) detection techniques, which can identify…
We introduce a new scene graph generation method called image-level attentional context modeling (ILAC). Our model includes an attentional graph network that effectively propagates contextual information across the graph using image-level…
As parallelism becomes critically important in the semiconductor technology, high-performance computing, and cloud applications, parallel network systems will increasingly follow suit. Today, parallelism is an essential architectural…
Graph machine learning, particularly using graph neural networks, heavily relies on node features. However, many real-world systems, such as social and biological networks, lack node features due to privacy concerns, incomplete data, or…