Instantly Decodable Network Coding

Instantly Decodable Network Coding
Author: Mohammad Shahedul Karim
Publisher:
Total Pages: 0
Release: 2017
Genre:
ISBN:

The network coding paradigm enhances transmission efficiency by combining information flows and has drawn significant attention in information theory, networking, communications and data storage. Instantly decodable network coding (IDNC), a subclass of network coding, has demonstrated its ability to improve the quality of service of time critical applications thanks to its attractive properties, namely the throughput enhancement, delay reduction, simple XOR-based encoding and decoding, and small coefficient overhead. Nonetheless, for point to multi-point (PMP) networks, IDNC cannot guarantee the decoding of a specific new packet at individual devices in each transmission. Furthermore, for device-to-device (D2D) networks, the transmitting devices may possess only a subset of packets, which can be used to form coded packets. These challenges require the optimization of IDNC algorithms to be suitable for different application requirements and network configurations. In this thesis, we first study a scalable live video broadcast over a wireless PMP network, where the devices receive video packets from a base station. Such layered live video has a hard deadline and imposes a decoding order on the video layers. We design two prioritized IDNC algorithms that provide a high level of priority to the most important video layer before considering additional video layers in coding decisions. These prioritized algorithms are shown to increase the number of decoded video layers at the devices compared to the existing network coding schemes. We then study video distribution over a partially connected D2D network, where a group of devices cooperate with each other to recover their missing video content. We introduce a cooperation aware IDNC graph that defines all feasible coding and transmission conflictfree decisions. Using this graph, we propose an IDNC solution that avoids coding and transmission conflicts, and meets the hard deadline for high importance video packets. It is demonstrated that the proposed solution delivers an improved video quality to the devices compared to the video and cooperation oblivious coding schemes. We also consider a heterogeneous network wherein devices use two wireless interfaces to receive packets from the base station and another device concurrently. For such network, we are interested in applications with reliable in-order packet delivery requirements. We represent all feasible coding opportunities and conflict-free transmissions using a dual interface IDNC graph. We select a maximal independent set over the graph by considering dual interfaces of individual devices, in-order delivery requirements of packets and lossy channel conditions. This graph based solution is shown to reduce the in-order delivery delay compared to the existing network coding schemes. Finally, we consider a D2D network with a group of devices experiencing heterogeneous channel capacities. For such cooperative scenarios, we address the problem of minimizing the completion time required for recovering all missing packets at the devices using IDNC and physical layer rate adaptation. Our proposed IDNC algorithm balances between the adopted transmission rate and the number of targeted devices that can successfully receive the transmitted packet. We show that the proposed rate aware IDNC algorithm reduces the completion time compared to the rate oblivious coding schemes.

Completion Delay Minimization for Instantly Decodable Network Coding

Completion Delay Minimization for Instantly Decodable Network Coding
Author: Sameh Sorour
Publisher:
Total Pages: 422
Release: 2011
Genre:
ISBN: 9780494776438

Instantly Decodable Network Coding (IDNC) is a subclass of opportunistic network coding that has numerous desirable properties for a wide spectrum of applications, namely its faster decoding delay, simpler coding and decoding processes, and no decoding buffer requirements. Nonetheless, IDNC suffers from two main problems that may limit its attractiveness, as an implementable solution in future wireless networks, against full network coding (FNC), widely studied in the literature. First, it cannot guarantee the decoding of a new packet at each receiver in each transmission, which may severely affect its completion delay. Second, it requires full feedback in order to operate properly, which may be prohibitive for several practical network settings.To study the effect of feedback reduction, we formulate the completion delay minimization problem, for the cases of intermittent and lossy feedback, as extended SSP and partially observable SSP problems, respectively. We show that these new formulations have the same structure of the original SSP. We thus extend the designed algorithms to operate in intermittent and lossy feedback scenarios, after taking update decisions on the attempted and un-acknowledged packets. These redesigned algorithms are shown to achieve tolerable degradation for relatively low feedback frequencies and high feedback loss rates. iiiIn this thesis, we aim to reduce the effect of these drawbacks by studying the problems of minimizing the IDNC completion delay in full and limited feedback scenarios. Since completion delay cannot be optimized only through local decisions in each of the transmissions, we first study the evolution of the IDNC coding opportunities and determine the strategies maximizing them, not only for one transmission, but for all future transmissions. We then formulate the completion delay problem as a stochastic shortest path (SSP) problem, which turns out to be of extremely large dimensions that makes its optimal solution intractable. Nonetheless, we exploit the structure of this SSP and the evolution of the coding opportunities to design efficient algorithms, which outperform FNC in most multicast scenarios and achieve a near-optimal performance in broadcast scenarios. However, since FNC still outperforms IDNC in some network scenarios, we design an adaptive selection algorithm that efficiently selects, between these two schemes, the one that achieves the smaller completion delay.

Decodable Network Coding in Wireless Network

Decodable Network Coding in Wireless Network
Author: Junwei Su
Publisher:
Total Pages: 33
Release: 2017
Genre:
ISBN:

"Network coding is a network layer technique to improve transmission efficiency. Coding packets is especially beneficial in a wireless environment where the demand for radio spectrum is high. However, to fully realize the benefits of network coding two challenging issues that must be addressed are: (1) Guaranteeing separation of coded packets at the destination, and (2) Mitigating the extra coding/decoding delay. If the destination has all the needed packets to decode a coded packet, then separation failure can be averted. If the scheduling algorithm considers the arrival time of coding pairs, then the extra delay can be mitigated. In this paper, we develop a network coding method to address these (decoding and latency) issues for multi-source multi-destination unicast and multicast sessions. We use linear programming to find the most efficient coding design solution with guaranteed decodability. To reduce network delay, we develop a scheduling algorithm to minimize the extra coding/decoding delay. Our coding design method and scheduling algorithm are validated through experiments. Simulation results show improved transmission efficiency and reduced network delay"--Abstract, page iii.

Network Coding and Subspace Designs

Network Coding and Subspace Designs
Author: Marcus Greferath
Publisher: Springer
Total Pages: 443
Release: 2018-01-29
Genre: Technology & Engineering
ISBN: 3319702939

This book, written by experts from universities and major research laboratories, addresses the hot topic of network coding, a powerful scheme for information transmission in networks that yields near-optimal throughput. It introduces readers to this striking new approach to network coding, in which the network is not simply viewed as a mechanism for delivering packets, but rather an algebraic structure named the subspace, which these packets span. This leads to a new kind of coding theory, employing what are called subspace codes. The book presents selected, highly relevant advanced research output on: Subspace Codes and Rank Metric Codes; Finite Geometries and Subspace Designs; Application of Network Coding; Codes for Distributed Storage Systems. The outcomes reflect research conducted within the framework of the European COST Action IC1104: Random Network Coding and Designs over GF(q). Taken together, they offer communications engineers, R&D engineers, researchers and graduate students in Mathematics, Computer Science, and Electrical Engineering a comprehensive reference guide to the construction of optimal network codes, as well as efficient encoding and decoding schemes for a given network code.

Network Coding

Network Coding
Author: Khaldoun Al Agha
Publisher: John Wiley & Sons
Total Pages: 171
Release: 2012-12-27
Genre: Technology & Engineering
ISBN: 1118563107

Network coding, a relatively new area of research, has evolved from the theoretical level to become a tool used to optimize the performance of communication networks – wired, cellular, ad hoc, etc. The idea consists of mixing “packets” of data together when routing them from source to destination. Since network coding increases the network performance, it becomes a tool to enhance the existing protocols and algorithms in a network or for applications such as peer-to-peer and TCP. This book delivers an understanding of network coding and provides a set of studies showing the improvements in security, capacity and performance of fixed and mobile networks. This is increasingly topical as industry is increasingly becoming more reliant upon and applying network coding in multiple applications. Many cases where network coding is used in routing, physical layer, security, flooding, error correction, optimization and relaying are given – all of which are key areas of interest. Network Coding is the ideal resource for university students studying coding, and researchers and practitioners in sectors of all industries where digital communication and its application needs to be correctly understood and implemented. Contents 1. Network Coding: From Theory to Practice, Youghourta Benfattoum, Steven Martin and Khaldoun Al Agha. 2. Fountain Codes and Network Coding for WSNs, Anya Apavatjrut, Claire Goursaud, Katia Jaffrès-Runser and Jean-Marie Gorce. 3. Switched Code for Ad Hoc Networks: Optimizing the Diffusion by Using Network Coding, Nour Kadi and Khaldoun Al Agha. 4. Security by Network Coding, Katia Jaffrès-Runser and Cédric Lauradoux. 5. Security for Network Coding, Marine Minier, Yuanyuan Zhang and Wassim Znaïdi. 6. Random Network Coding and Matroids, Maximilien Gadouleau. 7. Joint Network-Channel Coding for the Semi-Orthogonal MARC: Theoretical Bounds and Practical Design, Atoosa Hatefi, Antoine O. Berthet and Raphael Visoz. 8. Robust Network Coding, Lana Iwaza, Marco Di Renzo and Michel Kieffer. 9. Flow Models and Optimization for Network Coding, Eric Gourdin and Jeremiah Edwards.

Network Coding Theory

Network Coding Theory
Author: Raymond W. Yeung
Publisher: Now Publishers Inc
Total Pages: 156
Release: 2006
Genre: Computers
ISBN: 1933019247

Provides a tutorial on the basics of network coding theory. Divided into two parts, this book presents a unified framework for understanding the basic notions and fundamental results in network coding. It is aimed at students, researchers and practitioners working in networking research.

Throughput and Delay Optimization of Linear Network Coding in Wireless Broadcast

Throughput and Delay Optimization of Linear Network Coding in Wireless Broadcast
Author: Mingchao Yu
Publisher:
Total Pages: 0
Release: 2016
Genre:
ISBN:

Linear network coding (LNC) is able to achieve the optimal throughput of packet-level wireless broadcast, where a sender wishes to broadcast a set of data packets to a set of receivers within its transmission range through lossy wireless links. But the price is a large delay in the recovery of individual data packets due to network decoding, which may undermine all the benefits of LNC. However, packet decoding delay minimization and its relation to throughput maximization have not been well understood in the network coding literature. Motivated by this fact, in this thesis we present a comprehensive study on the joint optimization of throughput and average packet decoding delay (APDD) for LNC in wireless broadcast. To this end, we reveal the fundamental performance limits of LNC and study the performance of three major classes of LNC techniques, including instantly decodable network coding (IDNC), generation-based LNC, and throughput-optimal LNC (including random linear network coding (RLNC)). Various approaches are taken to accomplish the study, including 1) deriving performance bounds, 2) establishing and modelling optimization problems, 3) studying the hardness of the optimization problems and their approximation, 4) developing new optimal and heuristic techniques that take into account practical concerns such as receiver feedback frequency and computational complexity. Key contributions of this thesis include: - a necessary and sufficient condition for LNC to achieve the optimal throughput of wireless broadcast; - the NP-hardness of APDD minimization; - lower bounds of the expected APDD of LNC under random packet erasures; - the APDD-approximation ratio of throughput-optimal LNC, which has a value of between 4/3 and 2. In particular, the ratio of RLNC is exactly 2; - a novel throughput-optimal, APDD-approximation, and implementation-friendly LNC technique; - an optimal implementation of strict IDNC that is robust to packet erasures; - a novel generation-based LNC technique that generalizes some of the existing LNC techniques and enables tunable throughput-delay tradeoffs.

Cognitive Networks

Cognitive Networks
Author: Jaime Lloret Mauri
Publisher: CRC Press
Total Pages: 496
Release: 2014-12-09
Genre: Computers
ISBN: 1482237008

A cognitive network makes use of the information gathered from the network in order to sense the environment, plan actions according to the input, and make appropriate decisions using a reasoning engine. The ability of cognitive networks to learn from the past and use that knowledge to improve future decisions makes them a key area of interest for