Decodable network coding in wireless network

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

Algebraic techniques for deterministic networks

We here summarize some recent advances in the study of linear deterministic networks, recently proposed as approximations for wireless channels. This work started by extending the algebraic framework developed for multicasting over graphs in [1] to include operations over matrices and to admit both graphs and linear deterministic networks as special cases. Our algorithms build on this generalized framework, and provide as special cases unicast and multicast algorithms for deterministic networks, as well as network code designs using structured matrices

Vector Network Coding Algorithms

We develop new algebraic algorithms for scalar and vector network coding. In vector network coding, the source multicasts information by transmitting vectors of length L, while intermediate nodes process and combine their incoming packets by multiplying them with L x L coding matrices that play a similar role as coding c in scalar coding. Our algorithms for scalar network jointly optimize the employed field size while selecting the coding coefficients. Similarly, for vector coding, our algorithms optimize the length L while designing the coding matrices. These algorithms apply both for regular network graphs as well as linear deterministic networks

Network coding for transport protocols

With the proliferation of smart devices that require Internet connectivity anytime, anywhere, and the recent technological advances that make it possible, current networked systems will have to provide a various range of services, such as content distribution, in a wide range of settings, including wireless environments. Wireless links may experience temporary losses, however, TCP, the de facto protocol for robust unicast communications, reacts by reducing the congestion window drastically and injecting less traffic in the network. Consequently the wireless links are underutilized and the overall performance of the TCP protocol in wireless environments is poor. As content delivery (i.e. multicasting) services, such as BBC iPlayer, become popular, the network needs to support the reliable transport of the data at high rates, and with specific delay constraints. A typical approach to deliver content in a scalable way is to rely on peer-to-peer technology (used by BitTorrent, Spotify and PPLive), where users share their resources, including bandwidth, storage space, and processing power. Still, these systems suffer from the lack of incentives for resource sharing and cooperation, and this problem is exacerbated in the presence of heterogenous users, where a tit-for-tat scheme is difficult to implement. Due to the issues highlighted above, current network architectures need to be changed in order to accommodate the users¿ demands for reliable and quality communications. In other words, the emergent need for advanced modes of information transport requires revisiting and improving network components at various levels of the network stack. The innovative paradigm of network coding has been shown as a promising technique to change the design of networked systems, by providing a shift from how data flows traditionally move through the network. This shift implies that data flows are no longer kept separate, according to the ¿store-and-forward¿ model, but they are also processed and mixed in the network. By appropriately combining data by means of network coding, it is expected to obtain significant benefits in several areas of network design and architecture. In this thesis, we set out to show the benefits of including network coding into three communication paradigms, namely point-topoint communications (e.g. unicast), point-to-multipoint communications (e.g. multicast), and multipoint-to-multipoint communications (e.g. peer-to-peer networks). For the first direction, we propose a network coding-based multipath scheme and show that TCP unicast sessions are feasible in highly volatile wireless environments. For point-to-multipoint communications, we give an algorithm to optimally achieve all the rate pairs from the rate region in the case of degraded multicast over the combination network. We also propose a system for live streaming that ensures reliability and quality of service to heterogenous users, even if data transmissions occur over lossy wireless links. Finally, for multipoint-to-multipoint communications, we design a system to provide incentives for live streaming in a peer-to-peer setting, where users have subscribed to different levels of quality. Our work shows that network coding enables a reliable transport of data, even in highly volatile environments, or in delay sensitive scenarios such as live streaming, and facilitates the implementation of an efficient incentive system, even in the presence of heterogenous users. Thus, network coding can solve the challenges faced by next generation networks in order to support advanced information transport.Postprint (published version

機械学習と通信のための劣モジュラ・スパース最適化手法

学位の種別: 課程博士審査委員会委員 : （主査）東京大学教授 岩田 覚, 東京大学教授 定兼 邦彦, 東京大学教授 山本 博資, 東京大学准教授 武田 朗子, 東京大学准教授 平井 広志University of Tokyo(東京大学

On the benefits of vector network coding

In vector network coding, the source multi- casts information by transmitting vectors of length L, while intermediate nodes process and combine their incoming packets by multiplying them with L × L coding matrices that play a similar role as coding coefficients in scalar coding. Vector network coding generalizes scalar coding, and thus offers a wider range of solutions over which to optimize. This paper starts exploring the new possibilities vector network coding can offer along two directions. First, we propose a new randomized algorithm for vector network coding. We compare the performance of our proposed algorithm with the existing randomized al- gorithms in the literature over a specific class of networks. Second, we explore the use of structured coding matrices for vector network coding. We present deterministic de- signs that allow to operate using rotation coding matrices and thus result in reduced encoding complexity