The Internet has grown enormously since the introduction of the World Wide Web in the early 90's. The evolution and wide spread deployment of cellular networks have contributed to make the Internet accessible to more people in more places. The cellular networks of today offer data rates high enough for most Internet services. Even so, the service quality experienced by the users is often lower than in wired networks. The performance of TCP has a large impact on user experience. Therefore, we investigate TCP in cellular networks and propose functionality to improve the situation for TCP. We have studied sources of delay and data loss, such as link layer retransmissions, queuing, and handover. Measurements were conducted in a GSM/GPRS testbed. The results indicate that TCP interact efficiently with the GSM link layer protocol in most cases. From experiments of queuing in GPRS, we conclude that with a smaller buffer delay is reduced significantly, but that TCP throughput is about the same as with a larger buffer. Furthermore, we propose an improved buffer management when a connection loses all its resources to traffic with higher priority. We also propose a scheme for data forwarding to avoid negative impact on TCP during handover for WINNER, a research system that was used to test ideas for LTE. The achievable data rates in cellular networks are limited by inter-cell interference that vary over the cell. Inter-cell interference can be mitigated with Coordinated Multipoint techniques (CoMP), techniques that currently are being standardized for LTE-Advanced. System wide CoMP is, however, not an option, since it would be too resource consuming. In order to limit the required resources for CoMP, we propose an approach to select a subset of users for CoMP that is based on user experience. Simulation results indicate that user experience, represented with application utility,  and fairness are improved compared to if only rate is considered in the user selection. / The Internet has grown enormously since the introduction of the World Wide Web in the early 90's. The evolution and wide spread deployment of cellular networks have contributed to make the Internet accessible to more people in more places. The cellular networks of today offer data rates high enough for most Internet services. Even so, the service quality experienced by the users is often lower than in wired networks. The performance of TCP has a large impact on user experience. Therefore, we investigate TCP in cellular networks and propose functionality to improve the situation for TCP. We have studied sources of delay and data loss, such as link layer retransmissions, queuing, and handover. The achievable data rates in cellular networks are limited by inter-cell interference that vary over the cell area. Inter-cell interference can be mitigated with Coordinated Multipoint techniques (CoMP), techniques that currently are being standardized for LTE-Advanced. System wide CoMP is, however, not an option, since it would be too resource consuming. In order to limit the required resources for CoMP, we propose an approach to select a subset of the users for CoMP that is based on user experience.

The HF band of radio frequencies, from 3-30 MHz, is unique in its property that it is refracted by the ionosphere. This property allows long distance radio telecommunications around the world without requiring infrastructure. High frequency (HF) communication has been largely superseded by satellite and cellular technologies for day-to-day communications, due to the tight bandwidth constraints and technical difficulties inherent in using it. However there is still a need for HF communications devices where existing infrastructure is not available, such as in remote or polar locations, or in emergency situations due to natural disasters. This research is aimed at the development of an asymmetric HF communications link, with a battery-powered remote unit that transmits a small amount of data to a mains-powered base station. New technologies are identified and evaluated for use in the link, with the aim of reducing the power requirements of the remote unit. Error correction techniques are investigated. Low-density parity check (LDPC) codes, which are powerful codes used for forward error correction, are suggested for use in the link. Quasi-cyclic LDPC codes allow the low-power transmitter unit to use a computationally simple encoder based on feedback shift registers for generating the LDPC block codes cheaply. Semi-blind LDPC turbo equalisation is a powerful technique that can be used at the base station which utilises the structure of the LDPC code to encode the data stream. This equalises a received signal with a minimal amount of training data required, reducing the duty cycle of the remote unit. Hybrid automatic repeat request (HARQ) techniques are also investigated, which increase the throughput of a link when data repeats are required. A novel HARQ techniquewas created and proven to increase throughput in links with noise. As the proposed system may be deployed in remote locations, or locations where it might be difficult or undesirable to erect a proper HF antenna, a selection of buried antennas are characterised. A design for a remote unit is suggested. This unit was manufactured and used to test the capability of inexpensive, low power hardware to implement the proposed remote unit algorithms.

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.

Dans la plupart des systèmes radio, la correction des erreurs de transmission se base sur un protocole ARQ hybride (HARQ), qui combine un code correcteur d'erreurs (FEC) avec un protocole de retransmission (ARQ). La grande majorité des études portant sur l'HARQ se focalise essentiellement sur l'analyse et l'optimisation du code correcteur d'erreurs. Dans cette thèse, nous proposons une optimisation conjointe inter-couches du protocole ARQ hybride, en considérant à la fois des aspects de communications numériques et des aspects protocolaires. Dans les schémas HARQ classiques, dits simple paquet (SP), les paquets de redondance transmis sont tous relatifs au même paquet de données. Chaque retransmission réduit alors significativement le débit utile lorsque la taille des paquets est fixe. D'autre part, le débit est également très sensible à la présence d¿erreurs sur la voie retour. Nous proposons donc une nouvelle approche dite multi-paquets (MP), qui vise à réduire le nombre moyen de retransmissions par paquet grâce à la construction de paquets de redondance pouvant aider au décodage simultané de plusieurs paquets de données. Cette approche offre un gain significatif en débit par rapport aux schémas HARQ-SP lorsque le rapport signal à bruit (RSB) est suffisamment élevé. Les schémas HARQ classiques demeurent toutefois plus performants à faible RSB. Nous montrons par ailleurs que l'approche MP est plus robuste aux erreurs sur la voie de retour que l'approche SP. Ces résultats nous ont conduits à proposer un protocole hybride SP/MP qui bénéficie des avantages propres à chaque stratégie. Nous montrons par une analyse théorique ainsi que par simulation, que ce schéma hybride réalise un très bon compromis entre SP et MP sur toute la plage de RSB.

車載網路在未來社會將會越來越普及，車載網路資料傳遞技術，不僅能連接上網提供多元化的網路應用，甚至提供行車安全性服務。然而車載網路的網路品質不是十分理想，乃因存在著許多不確定因素，因為網路品質低，所以需要更完善的機制來提升車載網路品質。如車輛間速度不一，車輛間的距離變化相當頻繁，若因此而需要重新建立連線路徑是相當浪費傳輸時間並影響到傳輸的頻寛。本篇論文提出Cookie-Cooperative Automatic Repeat request (CCARQ) 的機制是針對這類型的間歇性斷線做修補，其方法是透過MAC (Media Access Control) 層協助網路層 (Network Layer) 的轉送。因為車輛是行駛在道路上，所建立的車載網路拓樸是沿著道路佈建，所以當連線路徑中斷時，周圍鄰近的車輛將是最好的替代選擇。本模擬將比較CCARQ, AODV與Bypass-AODV，透過 packet delivery ratio、end to end delay、control overhead及goodput為效能指標，調整在不同的hop數、車輛數目及車輛速度來觀察效能表現。模擬結果顯示packet delivery ratio增加14.6%、end to end delay減少41.3%、control overhead減少38%。 / VANET (Vehicular Ad-hoc Network) is expected to become more popular in the near future. VANET transmission technology will offer not only bountiful network applications but also safety driving information. Network quality of VANET is usually bad due to speed variation, high mobility, driver behavior variation, traffic density, traffic signal, environment complexity and swarm effect, et al. In this research, we propose a Cookie-Cooperative Automatic Repeat reQues (CCARQ) to enhance network performance by repairing intermittent broken connection. As mentioned above, speed variation will dynamically change the distances of car-to-car frequently and result in broken connections. Each re-establishment of broken connections will waste transmission time and reduce transmission bandwidth. The proposed CCARQ mechanism will help network layer transmission through MAC layer. Since VANET’s topology deployed along with road, the surrounded cars will be the best candidate to be a substitute car when a broken connection occurs. The performance indices are set to varied hops, varied speed and number of cars. Simulations show that CCARQ outperforms AODV and Bypass-AODV in packet delivery ratio by 14.6% increase, end to end delay by 41.3% decrease and control overhead by 38% decrease.

Approved for public release, distribution is unlimited / A recent improvement to the Seaweb underwater wireless network was the implementation of a Selective Automatic Repeat Request (SRQ) mechanism. SRQ is a protocol implemented in the Seaweb link layer as a measure for mitigating unreliability inherent in the telesonar physical layer. In January 2004, an experiment was performed in St. Andrew's Bay, Panama City, Florida. The goal was to transmit large data files through the network, in accordance with a Naval Special Warfare need for imagery file telemetry. For three point-to-point test geometries, SRQ was tested with a noisy and variable physical layer. Through the incorporation of SRQ, the unreliability was overcome. A link-budget model calibrated with the sound channel data collected from the experiment establishes the benefit of a "SRQ gain." / Ensign, United States Navy

The theory of lattices --- a mathematical approach for representing infinite discrete points in Euclidean space, has become a powerful tool to analyze many point-to-point digital and wireless communication systems, particularly, communication systems that can be well-described by the linear Gaussian vector channel model. This is mainly due to the three facts about channel codes constructed using lattices: they have simple structure, their ability to achieve the fundamental limits (the capacity) of the channel, and most importantly, they can be decoded using efficient decoders called lattice decoders. Since its introduction to multiple-input multiple-output (MIMO) wireless communication systems, sphere decoders has become an attractive efficient implementation of lattice decoders, especially for small signal dimensions and/or moderate to large signal-to-noise ratios (SNRs). In the first part of this dissertation, we consider sphere decoding algorithms that describe lattice decoding. The exact complexity analysis of the basic sphere decoder for general space-time codes applied to MIMO wireless channel is known to be difficult. Characterizing and understanding the complexity distribution is important, especially when the sphere decoder is used under practically relevant runtime constraints. In this work, we shed the light on the (average) computational complexity of sphere decoding for the quasi-static, LAttice Space-Time (LAST) coded MIMO channel. Sphere decoders are only efficient in the high SNR regime and low signal dimensions, and exhibits exponential (average) complexity for low-to-moderate SNR and large signal dimensions. On the other extreme, linear and non-linear receivers such as minimum mean-square error (MMSE), and MMSE decision-feedback equalization (DFE) are considered attractive alternatives to sphere decoders in MIMO channels. Unfortunately, the very low decoding complexity advantage that these decoders can provide comes at the expense of poor performance, especially for large signal dimensions. The problem of designing low complexity receivers for the MIMO channel that achieve near-optimal performance is considered a challenging problem and has driven much research in the past years. The problem can solved through the use of lattice sequential decoding that is capable of bridging the gap between sphere decoders and low complexity linear decoders (e.g., MMSE-DFE decoder). In the second part of this thesis, the asymptotic performance of the lattice sequential decoder for LAST coded MIMO channel is analyzed. We determine the rates achievable by lattice coding and sequential decoding applied to such a channel. The diversity-multiplexing tradeoff under such a decoder is derived as a function of its parameter--- the bias term. In this work, we analyze both the computational complexity distribution and the average complexity of such a decoder in the high SNR regime. We show that there exists a cut-off multiplexing gain for which the average computational complexity of the decoder remains bounded. Our analysis reveals that there exists a finite probability that the number of computations performed by the decoder may become excessive, even at high SNR, during high channel noise. This probability is usually referred to as the probability of a decoding failure. Such probability limits the performance of the lattice sequential decoder, especially for a one-way communication system. For a two-way communication system, such as in MIMO Automatic Repeat reQuest (ARQ) system, the feedback channel can be used to eliminate the decoding failure probability. In this work, we modify the lattice sequential decoder for the MIMO ARQ channel, to predict in advance the occurrence of decoding failure to avoid wasting the time trying to decode the message. This would result in a huge saving in decoding complexity. In particular, we will study the throughput-performance-complexity tradeoffs in sequential decoding algorithms and the effect of preprocessing and termination strategies. We show, analytically and via simulation, that using the lattice sequential decoder that implements a simple yet efficient time-out algorithm for joint error detection and correction, the optimal tradeoff of the MIMO ARQ channel can be achieved with significant reduction in decoding complexity.

Wireless broadband technologies provide ubiquitous broadband access to wireless users, enabling services that were available only to wireline users. At the same time, with the steady growth of real-time streaming applications such as video on demand (VoD), voice over IP (VoIP), massive online gaming, and so forth, the IEEE 802.16 standard (commonly known as WiMAX) has emerged as one of the strongest contenders to provide such kind of broadband wireless access services. WiMAX has specified some advanced lard features at physical (PHY) layer techniques and media access control (MAC) layer protocols, which adopted many Quality of Service (QoS) scheduling algorithms, resource allocation, Hybrid ARQ and so on. Moreover, forward error correction (FEC) and automatic repeat request (ARQ) techniques have already specified in the standard and they are used to support real-time streaming services in all kind of channel conditions. Inside this thesis, we propose an efficient fast feedback algorithm and buffer congestion control improvement scheme for data streaming over WiMAX networks. Two reserved bits in Generic MAC header (GMH) of each MPDU in WiMAX systems are utilized. The first reserved bit is used as a trigger in fast feedback strategy to add more robust coding and choose better feedback channel when the feedback message does not arrive properly within its cycle time trip (CTT) or after timeout. The second reserved bit is used to inform the base station about the serving subscriber stations¡¦ buffers states when their buffers are above the predefine-threshold value. Increasing number of retrieval of the feedback message, which means we can increase restore-bit-error probabilities within each frame, and then the throughput performance shall increase as well. In addition, by having each subscriber stations or service station¡¦s buffer states knowledge, the BS will be able to provide extra bandwidth allocation to the SSs more efficiency and accuracy. Keywords: WiMAX, PHY, MAC, Hybrid ARQ, QoS, Generic MAC header, CTT, Fast Feedback, Buffer Congestion Control Improvement, Real-Time streaming.

ITC/USA 2012 Conference Proceedings / The Forty-Eighth Annual International Telemetering Conference and Technical Exhibition / October 22-25, 2012 / Town and Country Resort & Convention Center, San Diego, California / As the integrated Network Enhanced Telemetry (iNET) program prepares for developmental flights tests, refinements are being made to the Radio Access Network Standard that ensures interoperability of networked radio components. One key aspect of this interoperability is the definition of Telemetry Network System (TmNS) RF Data Link Layer functionality for conducting efficient communications between radios in a TDMA (Time Division Multiple Access) channel sharing scheme. This paper examines the overall structure of the TmNS RF Data Link Layer and provides an overview of its operation. Specific topics include Medium Access Control (MAC) scheduling and framing in the context of a burst-oriented TDMA structure, link layer encryption, the priority-enabled Automatic Repeat reQuest (ARQ) protocol, high-level network packet and link control message encapsulation, payload segmentation and reassembly, and radio Link Layer Control Messaging.