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Next, we investigate several models of TRC including the discrete memoryless TRC, the Gaussian TRC and the bandlimited Gaussian TRC, and prove an outer bound on the capacity region of each of the TRC models. In particular, the outer bound on the capacity region of the bandlimited Gaussian TRC is a theoretical outer bound on the capacity region achievable by physical-layer network coding (PNC). Furthermore, we model a cellular relay network consisting of multiple users, multiple relays and multiple base stations as a collection of two-node point-to-point systems and three-node networks, where each two-node point-to-point system consists of two bandlimited Gaussian channels and each three-node network consists of a bandlimited Gaussian TRC. We obtain performance bounds of PNC on the cellular relay network by simulation and our simulation results show that the average maximum equal-rate throughput over all users under every PNC strategy investigated is generally worse than the average equal-rate throughput over all users under some routing strategy. This is possibly due to larger interference among the nodes under the PNC strategies compared with the routing strategy. / Our investigation of wireless relay networks begins by studying the two-way relay channel (TRC), in which a user and a base station exchange their messages with the help of a middle relay. We model the TRC as a three-node point-to-point relay network and propose practical symbol-level network coding schemes for the three-node network. We obtain several rate regions achievable by the network coding schemes and show that the use of symbol-level network coding rather than routing alone always enlarges the achievable rate region. Inparticular, the use of symbol-level network coding always increases the maximum equal-rate throughput. Furthermore, we model a cellular relay network consisting of multiple users, multiple relays and multiple base stations as a collection of two-node point-to-point systems and three-node point-to-point relay networks where each point-to-point channel is modeled as a bandlimited Gaussian channel. We propose several practical symbol-level network coding schemes on the network and investigate the benefit of symbol-level network coding by simulation. Our simulation results show that the use of symbol-level network coding rather than routing alone increases the average maximum equal-rate throughput over all users. / Fong, Lik Hang Silas. / Adviser: Yeung, Wai-Ho Raymond. / Source: Dissertation Abstracts International, Volume: 73-04, Section: B, page: . / Thesis (Ph.D.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 267-270). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [201-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

Tan, Min. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (p. 35-36). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Previous Work --- p.1 / Chapter 1.2 --- Motivation --- p.2 / Chapter 1.3 --- Contributions --- p.2 / Chapter 1.4 --- Thesis Organization --- p.3 / Chapter 2 --- Linear Network Coding Basics --- p.5 / Chapter 2.1 --- Formulation and Example --- p.5 / Chapter 2.2 --- Some Notations --- p.9 / Chapter 3 --- A Unified Framework --- p.13 / Chapter 3.1 --- Generic Network Codes Revisited --- p.13 / Chapter 3.2 --- A Unified Framework --- p.24 / Chapter 3.3 --- Simplified Proofs --- p.29 / Chapter 4 --- Conclusion --- p.33 / Bibliography --- p.35

Mi, Wengang. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 80-85). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Path loss and fading channel --- p.2 / Chapter 1.2 --- Relay Channel --- p.4 / Chapter 1.3 --- Power allocation --- p.6 / Chapter 1.4 --- Network coding --- p.8 / Chapter 1.5 --- Outline of the thesis --- p.8 / Chapter 2 --- Background Study --- p.10 / Chapter 2.1 --- Cooperative communication --- p.10 / Chapter 2.1.1 --- User cooperation diversity --- p.11 / Chapter 2.1.2 --- Cooperative diversity --- p.14 / Chapter 2.1.3 --- Coded cooperation --- p.18 / Chapter 2.2 --- Power control and resource allocation in cooperative communication --- p.19 / Chapter 2.3 --- Network coding --- p.21 / Chapter 3 --- Power allocation in DF system --- p.24 / Chapter 3.1 --- Introduction --- p.24 / Chapter 3.2 --- System Model --- p.25 / Chapter 3.3 --- BER analysis with power allocation --- p.27 / Chapter 3.3.1 --- BER analysis of single relay system --- p.27 / Chapter 3.3.2 --- Generalization for N-relay cooperation system --- p.30 / Chapter 3.4 --- Approximation --- p.31 / Chapter 3.5 --- Conclusion --- p.37 / Chapter 4 --- Network coding cooperation --- p.38 / Chapter 4.1 --- Introduction --- p.38 / Chapter 4.2 --- System model --- p.39 / Chapter 4.3 --- Performance analysis --- p.44 / Chapter 4.3.1 --- Network coding cooperation --- p.47 / Chapter 4.3.2 --- Conventional repetition cooperation --- p.48 / Chapter 4.3.3 --- Simulation result --- p.49 / Chapter 4.4 --- More nodes with network coding --- p.52 / Chapter 4.4.1 --- System model: to be selfish or not --- p.53 / Chapter 4.4.2 --- Performance analysis --- p.56 / Chapter 4.4.3 --- Simulation result --- p.62 / Chapter 4.5 --- Further discussion --- p.63 / Chapter 5 --- Conclusion --- p.64 / Chapter A --- Equation Derivation --- p.66 / Chapter A.l --- Proof of proposition 1 --- p.66 / Chapter A.2 --- Generalized solution --- p.68 / Chapter A.3 --- System outage probability of generous scheme --- p.69 / Chapter A.4 --- System outage probability of selfish scheme --- p.74 / Bibliography --- p.79

Fong, Lik Hang Silas. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (leaves 40). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Linear Network Code --- p.4 / Chapter 2.1 --- Linear Network Code without Link Failures --- p.4 / Chapter 2.1.1 --- Linear Multicast and Linear Broadcast --- p.6 / Chapter 2.2 --- Linear Network Code with Link Failures --- p.8 / Chapter 2.2.1 --- Static Linear Multicast and Static Linear Broadcast --- p.9 / Chapter 3 --- Variable-Rate Linear Network Coding --- p.11 / Chapter 3.1 --- Variable-Rate Linear Network Coding without Link Failures --- p.11 / Chapter 3.1.1 --- Problem Formulation --- p.11 / Chapter 3.1.2 --- Algorithm and Analysis --- p.12 / Chapter 3.2 --- Variable-Rate Linear Network Coding with Link Failures --- p.23 / Chapter 3.2.1 --- Problem Formulation --- p.23 / Chapter 3.2.2 --- Algorithm and Analysis --- p.23 / Chapter 3.3 --- The Maximum Broadcast Rate of Linear Network Code --- p.28 / Chapter 4 --- Conclusion --- p.38 / Bibliography --- p.40

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A dissertation submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Master of Science in Engineering, 2016 / The design and implementation of network coding into a location based ad- dressing algorithm for VANET has been investigated. Theoretical analysis of the network coding algorithm has been done by using a simplified topology called the ladder topology. The theoretical models were shown to describe the way that network coding and standard location based addressing works over the VANET network. All tests were performed over simulation. Network coding was shown to improve performance by a factor of 1.5 to 2 times in both simulation and theoretical models. The theoretical models demonstrate a fundamental limit to how much network coding can improve performance by, and these were confirmed by the simulations. Network coding does have a susceptibility to interference, but the other benefits of the techniques are substantial despite this. Network coding demonstrates strong possibilities for future development for VANET protocols. The ladder topology is an important tool for future analysis. / GS

NETI@home, a distributed network measurement infrastructure to collect passive end-to-end network measurements from Internet end-hosts was developed and discussed. The data collected by this infrastructure, as well as other datasets, were used to conduct studies on the behavior of the network and network users as well as the security issues affecting the Internet. A flow-based comparison of honeynet traffic, representing malicious traffic, and NETI@home traffic, representing typical end-user traffic, was conducted. This comparison showed that a large portion of flows in both datasets were failed and potentially malicious connection attempts. We additionally found that worm activity can linger for more than a year after the initial release date. Malicious traffic was also found to originate from across the allocated IP address space. Other security-related observations made include the suspicious use of ICMP packets and attacks on our own NETI@home server. Utilizing observed TTL values, studies were also conducted into the distance of Internet routes and the frequency with which they vary. The frequency and use of network address translation and the private IP address space were also discussed. Various protocol options and flags were analyzed to determine their adoption and use by the Internet community. Network-independent empirical models of end-user network traffic were derived for use in simulation. Two such models were created. The first modeled traffic for a specific TCP or UDP port and the second modeled all TCP or UDP traffic for an end-user. These models were implemented and used in GTNetS. Further anonymization of the dataset and the public release of the anonymized data and their associated analysis tools were also discussed.

The emergence of new Internet applications and technologies have resulted in an increased complexity as well as a need for lower latency, higher bandwidth, and increased reliability. This ultimately results in an increased complexity of network operation and management. Manual management is not sufficient to meet these new requirements. There is a need for data driven techniques to advance from manual management to autonomous management of network systems. One such technique, Machine Learning (ML), can use data to create models from hidden patterns in the data and make autonomous modifications. This approach has shown significant improvements in other domains (e.g., image recognition and natural language processing). The use of ML, along with advances in programmable control of Software- Defined Networks (SDNs), will alleviate manual network intervention and ultimately aid in autonomous network operations. However, realizing a data driven system that can not only understand what is happening in the network but also operate autonomously requires advances in the networking domain, as well as in ML algorithms. In this thesis, we focus on developing ML-based network architectures and data driven net- working algorithms whose objective is to improve the performance and management of future networks and network applications. We focus on problems spanning across the network protocol stack from the application layer to the physical layer. We design algorithms and architectures that are motivated by measurements and observations in real world or experimental testbeds. In Part I we focus on the challenge of monitoring and estimating user video quality of experience (QoE) of encrypted video traffic for network operators. We develop a system for REal-time QUality of experience metric detection for Encrypted Traffic, Requet. Requet uses a detection algorithm to identify video and audio chunks from the IP headers of encrypted traffic. Features extracted from the chunk statistics are used as input to a random forest ML model to predict QoE metrics. We evaluate Requet on a YouTube dataset we collected, consisting of diverse video assets delivered over various WiFi and LTE network conditions. We then extend Requet, and present a study on YouTube TV live streaming traffic behavior over WiFi and cellular networks covering a 9-month period. We observed pipelined chunk requests, a reduced buffer capacity, and a more stable chunk duration across various video resolutions compared to prior studies of on-demand streaming services. We develop a YouTube TV analysis tool using chunks statistics detected from the extracted data as input to a ML model to infer user QoE metrics. In Part II we consider allocating end-to-end resources in cellular networks. Future cellular networks will utilize SDN and Network Function Virtualization (NFV) to offer increased flexibility for network infrastructure operators to utilize network resources. Combining these technologies with real-time network load prediction will enable efficient use of network resources. Specifically, we leverage a type of recurrent neural network, Long Short-Term Memory (LSTM) neural networks, for (i) service specific traffic load prediction for network slicing, and (ii) Baseband Unit (BBU) pool traffic load prediction in a 5G cloud Radio Access Network (RAN). We show that leveraging a system with better accuracy to predict service requirements results in a reduction of operation costs. We focus on addressing the optical physical layer in Part III. Greater network flexibility through SDN and the growth of high bandwidth services are motivating faster service provisioning and capacity management in the optical layer. These functionalities require increased capacity along with rapid reconfiguration of network resources. Recent advances in optical hardware can enable a dramatic reduction in wavelength provisioning times in optical circuit switched networks. To support such operations, it is imperative to reconfigure the network without causing a drop in service quality to existing users. Therefore, we present a ML system that uses feedforward neural networks to predict the dynamic response of an optically circuit-switched 90-channel multi-hop Reconfigurable Optical Add-Drop Multiplexer (ROADM) network. We show that the trained deep neural network can recommend wavelength assignments for wavelength switching with minimal power excursions. We extend the performance of the ML system by implementing and testing a Hybrid Machine Learning (HML) model, which combines an analytical model with a neural network machine learning model to achieve higher prediction accuracy. In Part IV, we use a data-driven approach to address the challenge of wireless content delivery in crowded areas. We present the Adaptive Multicast Services (AMuSe) system, whose objective is to enable scalable and adaptive WiFi multicast. Specifically, we develop an algorithm for dynamic selection of a subset of the multicast receivers as feedback nodes. Further, we describe the Multicast Dynamic Rate Adaptation (MuDRA) algorithm that utilizes AMuSe’s feedback to optimally tune the physical layer multicast rate. Our experimental evaluation of MuDRA on the ORBIT testbed shows that MuDRA outperforms other schemes and supports high throughput multicast flows to hundreds of nodes while meeting quality requirements. We leverage the lessons learned from AMuSe for WiFi and use order statistics to address the performance issues with LTE evolved Multimedia Broadcast/Multicast Service (eMBMS). We present the Dynamic Monitoring (DyMo) system which provides low-overhead and real-time feedback about eMBMS performance to be used for network optimization. We focus on the Quality of Service (QoS) Evaluation module and develop a Two-step estimation algorithm which can efficiently identify the SNR Threshold as a one time estimation. DyMo significantly outperforms alternative schemes based on the Order-Statistics estimation method which relies on random or periodic sampling.