Physical layer network coding (PNC) was proposed under the two-way relay hannel (TWRC) scenario, where two sources exchange information aided by a relay. PNC allows the two sources to transmit to the relay simultaneously, where superimposed signals at the relay can be mapped to network-coded symbols and then be broadcast to both sources instead of being treated as interference. Concurrent transmissions using PNC achieve a higher spectrum efficiency compared to time division and network coding solutions. Existing research mainly focused on the symmetric PNC designs, where the same channel coding and modulation configurations are applied by both sources. When the channel conditions of the two source-relay links are asymmetric or unequal amount of data are exchanged, heterogeneous modulation PNC designs are necessary. In additional, the design and optimization of multi-hop PNC, where multiple relays forming a multi-hop path between the two sources, remains an open issue. The above issues motivate the study of this dissertation. This dissertation investigates the design of heterogeneous modulation physical layer network coding (HePNC), the integration of channel error control coding into HePNC, the combination of HePNC with hierarchical modulation, and the design and generalization of multi-hop PNC. The contributions of this dissertation are four-fold. First, under the asymmetric TWRC scenario, where the channel conditions of the two source-relay links are asymmetric, we designed a HePNC protocol, including the optimization of the adaptive mapping functions and the bit-symbol labeling, to minimize the end-to-end BER. In addition, we developed an analytical framework to derive the BER of HePNC. HePNC can substantially enhance the throughput compared to the existing symmetric PNC under the asymmetric TWRC scenario. Second, we investigated channel coded HePNC and integrated the channel error control coding into HePNC in a link-to-link coding, where the relay tries to decode the superimposed codewords in the multi-access stage. A full-state sum-product decoding algorithm is proposed at the relay based on the repeat-accumulate codes to guarantee reliable end-to-end communication. Third, we proposed hierarchical modulation PNC (H-PNC) under asymmetric TWRC, where additional data exchange between the relay and the source with the relatively better channel condition is achieved in addition to that between the two end sources, benefiting from superimposing the additional data flow on the PNC transmission. When the relay also has the data exchange requirement with the source with a better source-relay channel, H-PNC outperforms HePNC and PNC in terms of the system sum throughput. Fourth, we designed and generalized multi-hop PNC, where multiple relays located in a linear topology are scheduled to support the data exchange between two end sources. The impact of error propagation and mutual interference among the nodes are addressed and optimized. The proposed designs outperform the existing ones in terms of end-to-end BER and end-to-end throughout. / Graduate

The formal definition of network coding by Ahlswede et al. in 2000 has led to several breakthroughs in information theory, for example solving the bottleneck problem in butterfly networks and breaking the min-cut max-flow theorem for multicast communication. Especially promising is the usage of network coding as a packet-level Forward Error Correction (FEC) scheme to increase the robustness of a data stream against packet loss, also known as intra-session coding. Yet, despite these benefits, network coding-based FEC is still rarely deployed in real-world networks. To bridge this gap between information theory and real-world usage, this cumulative thesis will present our contributions to the integration, evaluation, and optimization of network coding-based FEC. The first set of contributions introduces and evaluates efficient ways to integrate coding into UDP-based IoT protocols to speed up bulk data transfers in lossy scenarios. This includes a packet-level FEC extension for the Constrained Application Protocol (CoAP) [P1] and one for MQTT for Sensor Networks (MQTT-SN), which levels the underlying publish-subscribe architecture [P2]. The second set of contributions addresses the development of novel evaluation tools and methods to better quantify possible coding gains. This includes link ’em, our award-winning link emulation bridge for reproducible networking research [P3], and also SPQER, a word recognition-based metric to evaluate the impact of packet loss on the Quality of Experience of Voice over IP applications [P5]. Finally, we highlight the impact of padding overhead for applications with heterogeneous packet lengths [P6] and introduce a novel packet-preserving coding scheme to significantly reduce this problem [P4]. Because many of the shown contributions can be applied to other areas of network coding research as well, this thesis does not only make meaningful contributions to specific network coding challenges, but also paves the way for future work to further close the gap between information theory and real-world usage.

This dissertation is directed towards improving the performance of 5G Wireless Fronthaul Networks and Wireless Sensor Networks, as measured by reliability, fault recovery time, energy consumption, efficiency, and security of transmissions, beyond what is achievable with conventional error control technology. To achieve these ambitious goals, the research is focused on novel applications of networking techniques, such as Diversity Coding, where a feedforward network design uses forward error control across spatially diverse paths to enable reliable wireless networking with minimal delay, in a wide variety of application scenarios. These applications include Cloud-Radio Access Networks (C-RANs), which is an emerging 5G wireless network architecture, where Remote Radio Heads (RRHs) are connected to the centralized Baseband Unit (BBU) via fronthaul networks, to enable near-instantaneous recovery from link/node failures. In addition, the ability of Diversity Coding to recover from multiple simultaneous link failures is demonstrated in many network scenarios. Furthermore, the ability of Diversity Coding to enable significantly simpler and thus lower-cost routing than other types of restoration techniques is demonstrated. Achieving high throughput for broadcasting/multicasting applications, with the required level of reliability is critical for the efficient operation of 5G wireless infrastructure networks. To improve the performance of C-RAN networks, a novel technology, Diversity and Network Coding (DC-NC), which synergistically combines Diversity Coding and Network Coding, is introduced. Application of DC-NC to several 5G fronthaul networks, enables these networks to provide high throughput and near-instant recovery in the presence of link and node failures. Also, the application of DC-NC coding to enhance the performance of downlink Joint Transmission-Coordinated Multi Point (JT-CoMP) in 5G wireless fronthaul C-RANs is demonstrated. In all these scenarios, it is shown that DC-NC coding can provide efficient transmission and reduce the resource consumption in the network by about one-third for broadcasting/multicasting applications, while simultaneously enabling near-instantaneous latency in recovery from multiple link/node failures in fronthaul networks. In addition, it is shown by applying the DC-NC coding, the number of redundant links that uses to provide the required level of reliability, which is an important metric to evaluate any protection system, is reduced by about 30%-40% when compared to that of Diversity Coding. With the additional goal of further reducing of the recovery time from multiple link/node failures and maximizing the network reliability, DC-NC coding is further improved to be able to tolerate multiple, simultaneous link failures with less computational complexity and lower energy consumption. This is accomplished by modifying Triangular Network Coding (TNC) and synergistically combining TNC with Diversity Coding to create enhanced DC-NC (eDC-NC), that is applied to Fog computing-based Radio Access Networks (F-RAN) and Wireless Sensor Networks (WSN). Furthermore, it is demonstrated that the redundancy percentage for protecting against n link failures is inversely related to the number of source data streams, which illustrates the scalability of eDC-NC coding. Solutions to enable synchronized broadcasting are proposed for different situations. The ability of eDC-NC coding scheme to provide efficient and secure broadcasting for 5G wireless F-RAN fronthaul networks is also demonstrated. The security of the broadcasting data streams can be obtained more efficiently than standardized methods such as Secure Multicasting using Secret (Shared) Key Cryptography.

No description available.

From its introduction to its quindecennial, network coding have built a strong reputation in enhancing packet recovery process and achieving maximum information flow in both wires and wireless networks. Traditional studies focused on optimizing the throughput of the network by proposing complex schemes that achieve optimal delay. With the shift toward distributed computing at mobile devices, throughput and complexity become both critical factors that affect the efficiency of a coding scheme. Instantly decodable network coding imposed itself as a new paradigm in network coding that trades off this two aspects. This paper presents a survey of instantly decodable network coding schemes that are proposed in the literature. The various schemes are identified, categorized and evaluated. Two categories can be distinguished namely the conventional centralized schemes and the distributed or cooperative schemes. For each scheme, the comparison is carried out in terms of reliability, performance, complexity and packet selection methodology. Although the performance is generally inversely proportional to the computation complexity, numerous successful schemes from both the performance and complexity viewpoint are identified.

Network Coding is a critical technique when designing next-generation network systems, since the use of network coding can significantly improve the throughput and performance (delay/reliability) of the system. In the traditional design paradigm without network coding, different information flows are transported in a similar way like commodity flows such that the flows are kept separated while being forwarded in the network. However, network coding allows nodes in the network to not only forward the packet but also process the incoming information messages with the goal of either improving the throughput, reducing delay, or increasing the reliability. Specifically, network coding is a critical tool when designing absolute Shannon-capacity-achieving schemes for various broadcasting and multi-casting applications. In this thesis, we study the optimal network schemes for some applications with less restrictive network models. A common component of the models/approaches is how to use network coding to take advantage of a broadcast communication channel.<div><br></div><div>In the first part of the thesis, we consider the system of one server transmitting K information flows, one for each of K users (destinations), through a broadcast packet erasure channels with ACK/NACK. The capacity region of 1-to-K broadcast packet erasure channels with ACK/NACK is known for some scenarios, e.g., K<=3, etc. However, existing achievability schemes with network coding either require knowing the target rate in advance, and/or have a complicated description of the achievable rate region that is difficult to prove whether it matches the capacity or not. In this part, we propose a new network coding protocol with the following features: (i) Its achievable rate region is identical to the capacity region for all the scenarios in which the capacity is known; (ii) Its achievable rate region is much more tractable and has been used to derive new capacity rate vectors; (iii) It employs sequential encoding that naturally handles dynamic packet arrivals; (iv) It automatically adapts to unknown packet arrival rates; (v) It is based on GF(q) with q>=K. Numerically, for K=4, it admits an average control overhead 1.1% (assuming each packet has 1000 bytes), average encoding memory usage 48.5 packets, and average per-packet delay 513.6 time slots, when operating at 95% of the capacity.</div><div><br></div><div>In the second part, we focus on the coded caching system of one server and K users, each user k has cache memory size M_k and demand a file among the N files currently stored at server. The coded caching system consists of two phases: Phase 1, the placement phase: Each user accesses the N files and fills its cache memory during off-peak hours; and Phase 2, the delivery phase: During the peak hours, each user submits his/her own file request and the server broadcasts a set of packet simultaneously to K users with the goal of successfully delivering the desired packets to each user. Due to the high complexity of coded caching problem with heterogeneous file size and heterogeneous cache memory size for arbitrary N and K, prior works focus on solving the optimal worst-case rate with homogeneous file size and mostly focus on designing order-optimal coded caching schemes with user-homogeneous file popularity that attain the lower bound within a constant factor. In this part, we derive the average rate capacity for microscopic 2-user/2-file (N=K=2) coded caching problem with heterogeneous files size, cache memory size, and user-dependent heterogeneous file popularity. The study will shed some further insights on the complexity and optimal scheme design of general coded caching problem with full heterogeneity.<br></div><div><br></div><div>In the third part, we further study the coded caching system of one server, K= 2 users, and N>=2 files and focus on the user-dependent file popularity of the two users. In order to approach the exactly optimal uniform average rate of the system, we simplify the file demand popularity to binary outputs, i.e., each user either has no interest (with probability 0) or positive uniform interest (with a constant probability) to each of the N file. Under this model, the file popularity of each user is characterized by his/her file demand set of positive interest in the N files. Specifically, we analyze the case of two user (K=2). We show the exact capacity results of one overlapped file of the two file demand sets for arbitrary N and two overlapped files of the two file demand sets for N = 3. To investigate the performance of large overlapped files we also present the average rate capacity under the constraint of selfish and uncoded prefetching with explicit prefetching schemes that achieve those capacities. All the results allow for arbitrary (and not necessarily identical) users' cache capacities and number of files in each file demand set.<br></div>

No description available.

Communication systems are experiencing a fundamental change. There are novel applications that require an increased performance not only of throughput but also latency, reliability, security, and heterogeneity support from these systems. To fulfil the requirements, future systems understand communication not only as the transport of bits but also as their storage, processing, and relation. In these systems, every network node has transport storage and computing resources that the network operator and its users can exploit through virtualisation and softwarisation of the resources. It is within this context that this work presents its results. We proposed distributed coded approaches to improve communication systems. Our results improve the reliability and latency performance of the transport of information. They also increase the reliability, flexibility, and throughput of storage applications. Furthermore, based on the lessons that coded approaches improve the transport and storage performance of communication systems, we propose a distributed coded approach for the computing of novel in-network applications such as the steering and control of cyber-physical systems. Our proposed approach can increase the reliability and latency performance of distributed in-network computing in the presence of errors, erasures, and attackers.

With increasing computational capabilities and advances in robotics, technology is at the verge of the next industrial revolution. An growing number of tasks can be performed by artificial intelligence and agile robots. This impacts almost every part of the economy, including agriculture, transportation, industrial manufacturing and even social interactions. In all applications of automated machines, communication is a critical component to enable cooperation between machines and exchange of sensor and control signals. The mobility and scale at which these automated machines are deployed also challenges todays communication systems. These complex cyber-physical systems consisting of up to hundreds of mobile machines require highly reliable connectivity to operate safely and efficiently. Current automation systems use wired communication to guarantee low latency connectivity. But wired connections cannot be used to connect mobile robots and are also problematic to deploy at scale. Therefore, wireless connectivity is a necessity. On the other hand, it is subject to many external influences and cannot reach the same level of reliability as the wired communication systems. This thesis aims to address this problem by proposing methods to combine multiple unreliable wireless connections to a stable channel. The foundation for this work is Caterpillar Random Linear Network Coding (CRLNC), a new variant of network code designed to achieve low latency. CRLNC performs similar to block codes in recovery of lost packets, but with a significantly decreased latency. CRLNC with Feedback (CRLNC-FB) integrates a Selective-Repeat ARQ (SR-ARQ) to optimize the tradeoff between delay and throughput of reliable communication. The proposed protocol allows to slightly increase the overhead to reduce the packet delay at the receiver. With CRLNC, delay can be reduced by more than 50 % with only a 10 % reduction in throughput. Finally, CRLNC is combined with a statistical multipath scheduler to optimize the reliability and service availability in wireless network with multiple unreliable paths. This multipath CRLNC scheme improves the reliability of a fixed-rate packet stream by 10 % in a system model based on real-world measurements of LTE and WiFi. All the proposed protocols have been implemented in the software library NCKernel. With NCKernel, these protocols could be evaluated in simulated and emulated networks, and were also deployed in several real-world testbeds and demonstrators.:Abstract 2 Acknowledgements 6 1 Introduction 7 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.2 Use Cases and Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.3 Opportunities of Multipath . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 1.4 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2 State of the Art of Multipath Communication 19 2.1 Physical Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.2 Data Link Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.3 Network Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.4 Transport Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.5 Application Layer and Session Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.6 Research Gap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3 NCKernel: Network Coding Protocol Framework 27 3.1 Theory that matters! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.2 Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 3.3 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.3.1 Socket Buffers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.3.2 En-/Re-/Decoder API . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.3.3 Configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.3.4 Timers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.3.5 Tracing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.4 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.5 Protocols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 4 Low-Latency Network Coding 35 4.1 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4.2 Random Linear Network Coding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 4.3 Low Latency Network Codes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 4.4 CRLNC: Caterpillar Random Linear Network Coding . . . . . . . . . . . . . . . . . . 38 4.4.1 Encoding and Packet Format . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 4.4.2 Decoding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 4.4.3 Computational Complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 4.5 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 4.5.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 4.5.2 Simulator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 4.5.3 Packet Loss Probability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 4.5.4 Delay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 4.5.5 Window Size Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 4.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 5 Delay-Throughput Tradeoff 55 5.1 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 5.2 Network Coding with ARQ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 5.3 CRLNC-FB: CRLNC with Feedback . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 5.3.1 Encoding and Packet Format . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 5.3.2 Decoding and Feedback . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 5.3.3 Retransmissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 5.4 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 5.4.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 5.4.2 Simulator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 5.4.3 Systematic Retransmissions . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 5.4.4 Coded Packet Memory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 5.4.5 Comparison with other Protocols . . . . . . . . . . . . . . . . . . . . . . . . 67 5.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 6 Multipath for Reliable Low-Latency Packet Streams 73 6.1 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 6.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 6.3 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 6.3.1 Traffic Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 6.3.2 Network Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 6.3.3 Channel Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 6.3.4 Reliability Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 6.4 Multipath CRLNC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 6.4.1 Window Size for Heterogeneous Paths . . . . . . . . . . . . . . . . . . . . . 77 6.4.2 Packet Scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 6.5 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 6.5.1 Simulator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 6.5.2 Preliminary Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 6.5.3 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 6.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 7 Conclusion 94 7.1 Results and Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 7.2 Future Research Topics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 Acronyms 99 Publications 101 Bibliography 103

No description available.