285 research outputs found
Reliable Packet Streams with Multipath Network Coding
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 10
Adaptive Causal Network Coding with Feedback for Multipath Multi-hop Communications
We propose a novel multipath multi-hop adaptive and causal random linear
network coding (AC-RLNC) algorithm with forward error correction. This
algorithm generalizes our joint optimization coding solution for point-to-point
communication with delayed feedback. AC-RLNC is adaptive to the estimated
channel condition, and is causal, as the coding adjusts the retransmission
rates using a priori and posteriori algorithms. In the multipath network, to
achieve the desired throughput and delay, we propose to incorporate an adaptive
packet allocation algorithm for retransmission, across the available resources
of the paths. This approach is based on a discrete water filling algorithm,
i.e., bit-filling, but, with two desired objectives, maximize throughput and
minimize the delay. In the multipath multi-hop setting, we propose a new
decentralized balancing optimization algorithm. This balancing algorithm
minimizes the throughput degradation, caused by the variations in the channel
quality of the paths at each hop. Furthermore, to increase the efficiency, in
terms of the desired objectives, we propose a new selective recoding method at
the intermediate nodes. We derive bounds on the throughput and the mean and
maximum in order delivery delay of AC-RLNC, both in the multipath and multipath
multi-hop case. In the multipath case, we prove that in the non-asymptotic
regime, the suggested code may achieve more than 90% of the channel capacity
with zero error probability. In the multipath multi-hop case, the balancing
procedure is proven to be optimal with regards to the achieved rate. Through
simulations, we demonstrate that the performance of our adaptive and causal
approach, compared to selective repeat (SR)-ARQ protocol, is capable of gains
up to a factor two in throughput and a factor of more than three in delay
Energy and rate allocation for massive multiple access with interference cancelation
This article addresses the problem of energy and code allocation to many users accessing, under spreading-based nonorthogonal multiple access, a wireless node set up with a successive interference cancellation architecture aided by redundancy-check error control. As an application, we consider the asynchronous access of a delay-tolerant satellite system, where users employ finite-length channel codes and are subject to a known power unbalance induced by the known distribution of the channel’s attenuation. The article develops, as a mathematically tractable approximation to massively populated systems, a unified framework to compute the best energy and code allocation rules that maximize the spectral efficiency of a network that handles asymptotically many users. Concretely, the presented approach circumvents the exponential complexity in the number of users when modeling the propagation of packet decoding failures through the receiver’s decoding scheme. It also enables a deterministic analysis of the more complex features affecting the receiver, making the related performance optimization problem amenable to systematic tools from differential and variational calculus. The derived expressions evidence the most favorable three-way unbalance between energy, rate, and reliability for receiver performance. Low-level system simulations are carried out for validation.This work was supported in part by the Spanish Ministry of Science and Innovation through project RODIN (PID2019-105717RB-C22/AEI/10.13039/501100011033) and in part by Grant 2017 SGR 578.Peer ReviewedPostprint (published version
A Survey of Air-to-Ground Propagation Channel Modeling for Unmanned Aerial Vehicles
In recent years, there has been a dramatic increase in the use of unmanned
aerial vehicles (UAVs), particularly for small UAVs, due to their affordable
prices, ease of availability, and ease of operability. Existing and future
applications of UAVs include remote surveillance and monitoring, relief
operations, package delivery, and communication backhaul infrastructure.
Additionally, UAVs are envisioned as an important component of 5G wireless
technology and beyond. The unique application scenarios for UAVs necessitate
accurate air-to-ground (AG) propagation channel models for designing and
evaluating UAV communication links for control/non-payload as well as payload
data transmissions. These AG propagation models have not been investigated in
detail when compared to terrestrial propagation models. In this paper, a
comprehensive survey is provided on available AG channel measurement campaigns,
large and small scale fading channel models, their limitations, and future
research directions for UAV communication scenarios
A Comprehensive Survey of the Tactile Internet: State of the art and Research Directions
The Internet has made several giant leaps over the years, from a fixed to a
mobile Internet, then to the Internet of Things, and now to a Tactile Internet.
The Tactile Internet goes far beyond data, audio and video delivery over fixed
and mobile networks, and even beyond allowing communication and collaboration
among things. It is expected to enable haptic communication and allow skill set
delivery over networks. Some examples of potential applications are
tele-surgery, vehicle fleets, augmented reality and industrial process
automation. Several papers already cover many of the Tactile Internet-related
concepts and technologies, such as haptic codecs, applications, and supporting
technologies. However, none of them offers a comprehensive survey of the
Tactile Internet, including its architectures and algorithms. Furthermore, none
of them provides a systematic and critical review of the existing solutions. To
address these lacunae, we provide a comprehensive survey of the architectures
and algorithms proposed to date for the Tactile Internet. In addition, we
critically review them using a well-defined set of requirements and discuss
some of the lessons learned as well as the most promising research directions
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