5,751 research outputs found
A Tutorial on Clique Problems in Communications and Signal Processing
Since its first use by Euler on the problem of the seven bridges of
K\"onigsberg, graph theory has shown excellent abilities in solving and
unveiling the properties of multiple discrete optimization problems. The study
of the structure of some integer programs reveals equivalence with graph theory
problems making a large body of the literature readily available for solving
and characterizing the complexity of these problems. This tutorial presents a
framework for utilizing a particular graph theory problem, known as the clique
problem, for solving communications and signal processing problems. In
particular, the paper aims to illustrate the structural properties of integer
programs that can be formulated as clique problems through multiple examples in
communications and signal processing. To that end, the first part of the
tutorial provides various optimal and heuristic solutions for the maximum
clique, maximum weight clique, and -clique problems. The tutorial, further,
illustrates the use of the clique formulation through numerous contemporary
examples in communications and signal processing, mainly in maximum access for
non-orthogonal multiple access networks, throughput maximization using index
and instantly decodable network coding, collision-free radio frequency
identification networks, and resource allocation in cloud-radio access
networks. Finally, the tutorial sheds light on the recent advances of such
applications, and provides technical insights on ways of dealing with mixed
discrete-continuous optimization problems
Network coding meets multimedia: a review
While every network node only relays messages in a traditional communication system, the recent network coding (NC) paradigm proposes to implement simple in-network processing with packet combinations in the nodes. NC extends the concept of "encoding" a message beyond source coding (for compression) and channel coding (for protection against errors and losses). It has been shown to increase network throughput compared to traditional networks implementation, to reduce delay and to provide robustness to transmission errors and network dynamics. These features are so appealing for multimedia applications that they have spurred a large research effort towards the development of multimedia-specific NC techniques. This paper reviews the recent work in NC for multimedia applications and focuses on the techniques that fill the gap between NC theory and practical applications. It outlines the benefits of NC and presents the open challenges in this area. The paper initially focuses on multimedia-specific aspects of network coding, in particular delay, in-network error control, and mediaspecific error control. These aspects permit to handle varying network conditions as well as client heterogeneity, which are critical to the design and deployment of multimedia systems. After introducing these general concepts, the paper reviews in detail two applications that lend themselves naturally to NC via the cooperation and broadcast models, namely peer-to-peer multimedia streaming and wireless networkin
Roadmap on optical security
Postprint (author's final draft
A Survey on Semantic Communications for Intelligent Wireless Networks
With deployment of 6G technology, it is envisioned that competitive edge of
wireless networks will be sustained and next decade's communication
requirements will be stratified. Also 6G will aim to aid development of a human
society which is ubiquitous and mobile, simultaneously providing solutions to
key challenges such as, coverage, capacity, etc. In addition, 6G will focus on
providing intelligent use-cases and applications using higher data-rates over
mill-meter waves and Tera-Hertz frequency. However, at higher frequencies
multiple non-desired phenomena such as atmospheric absorption, blocking, etc.,
occur which create a bottleneck owing to resource (spectrum and energy)
scarcity. Hence, following same trend of making efforts towards reproducing at
receiver, exact information which was sent by transmitter, will result in a
never ending need for higher bandwidth. A possible solution to such a challenge
lies in semantic communications which focuses on meaning (context) of received
data as opposed to only reproducing correct transmitted data. This in turn will
require less bandwidth, and will reduce bottleneck due to various undesired
phenomenon. In this respect, current article presents a detailed survey on
recent technological trends in regard to semantic communications for
intelligent wireless networks. We focus on semantic communications architecture
including model, and source and channel coding. Next, we detail cross-layer
interaction, and various goal-oriented communication applications. We also
present overall semantic communications trends in detail, and identify
challenges which need timely solutions before practical implementation of
semantic communications within 6G wireless technology. Our survey article is an
attempt to significantly contribute towards initiating future research
directions in area of semantic communications for intelligent 6G wireless
networks
Joint Source-channel Coding Using Machine Learning Techniques
Most modern communication systems rely on separate source encoding and channel encoding schemes to transmit data. Despite the long-lasting success of separate schemes, joint source channel coding schemes have been proven to outperform separate schemes in applications such as video communications. The task of this research is to develop a joint source-channel coding scheme that mitigates some of the limitations of current separate coding schemes. My research will attempt to leverage recent advances in machine/deep learning techniques to develop resilient schemes that do not depend on explicit codes for compression and error correction but automatically learn end-to-end mapping schemes for source signals. The success of the developed scheme will depend on its ability to correctly approximate an input vector under inconsistent channel conditions
Instantly Decodable Network Coding: From Centralized to Device-to-Device Communications
From its introduction to its quindecennial, network coding has built a strong reputation for enhancing packet recovery and achieving maximum information flow in both wired and wireless networks. Traditional studies focused on optimizing the throughput of the system by proposing elaborate schemes able to reach the network capacity. With the shift toward distributed computing on mobile devices, performance and complexity become both critical factors that affect the efficiency of a coding strategy. Instantly decodable network coding presents itself as a new paradigm in network coding that trades off these two aspects. This paper review instantly decodable network coding schemes by identifying, categorizing, and evaluating various algorithms proposed in the literature. The first part of the manuscript investigates the conventional centralized systems, in which all decisions are carried out by a central unit, e.g., a base-station. In particular, two successful approaches known as the strict and generalized instantly decodable network are compared in terms of reliability, performance, complexity, and packet selection methodology. The second part considers the use of instantly decodable codes in a device-to-device communication network, in which devices speed up the recovery of the missing packets by exchanging network coded packets. Although the performance improvements are directly proportional to the computational complexity increases, numerous successful schemes from both the performance and complexity viewpoints are identified
A Tutorial on Coding Methods for DNA-based Molecular Communications and Storage
Exponential increase of data has motivated advances of data storage
technologies. As a promising storage media, DeoxyriboNucleic Acid (DNA) storage
provides a much higher data density and superior durability, compared with
state-of-the-art media. In this paper, we provide a tutorial on DNA storage and
its role in molecular communications. Firstly, we introduce fundamentals of
DNA-based molecular communications and storage (MCS), discussing the basic
process of performing DNA storage in MCS. Furthermore, we provide tutorials on
how conventional coding schemes that are used in wireless communications can be
applied to DNA-based MCS, along with numerical results. Finally, promising
research directions on DNA-based data storage in molecular communications are
introduced and discussed in this paper
Machine Learning for Multimedia Communications
Machine learning is revolutionizing the way multimedia information is processed and transmitted to users. After intensive and powerful training, some impressive efficiency/accuracy improvements have been made all over the transmission pipeline. For example, the high model capacity of the learning-based architectures enables us to accurately model the image and video behavior such that tremendous compression gains can be achieved. Similarly, error concealment, streaming strategy or even user perception modeling have widely benefited from the recent learningoriented developments. However, learning-based algorithms often imply drastic changes to the way data are represented or consumed, meaning that the overall pipeline can be affected even though a subpart of it is optimized. In this paper, we review the recent major advances that have been proposed all across the transmission chain, and we discuss their potential impact and the research challenges that they raise
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