364 research outputs found
Error Propagation Mitigation in Sliding Window Decoding of Braided Convolutional Codes
We investigate error propagation in sliding window decoding of braided
convolutional codes (BCCs). Previous studies of BCCs have focused on iterative
decoding thresholds, minimum distance properties, and their bit error rate
(BER) performance at small to moderate frame length. Here, we consider a
sliding window decoder in the context of large frame length or one that
continuously outputs blocks in a streaming fashion. In this case, decoder error
propagation, due to the feedback inherent in BCCs, can be a serious problem.In
order to mitigate the effects of error propagation, we propose several schemes:
a \emph{window extension algorithm} where the decoder window size can be
extended adaptively, a resynchronization mechanism where we reset the encoder
to the initial state, and a retransmission strategy where erroneously decoded
blocks are retransmitted. In addition, we introduce a soft BER stopping rule to
reduce computational complexity, and the tradeoff between performance and
complexity is examined. Simulation results show that, using the proposed window
extension algorithm, resynchronization mechanism, and retransmission strategy,
the BER performance of BCCs can be improved by up to four orders of magnitude
in the signal-to-noise ratio operating range of interest, and in addition the
soft BER stopping rule can be employed to reduce computational complexity.Comment: arXiv admin note: text overlap with arXiv:1801.0323
Transitive and self-dual codes attaining the Tsfasman-Vladut-Zink bound
A major problem in coding theory is the question of whether the class of cyclic codes is asymptotically good. In this correspondence-as a generalization of cyclic codes-the notion of transitive codes is introduced (see Definition 1.4 in Section I), and it is shown that the class of transitive codes is asymptotically good. Even more, transitive codes attain the Tsfasman-Vladut-Zink bound over F-q, for all squares q = l(2). It is also shown that self-orthogonal and self-dual codes attain the Tsfasman-Vladut-Zink bound, thus improving previous results about self-dual codes attaining the Gilbert-Varshamov bound. The main tool is a new asymptotically optimal tower E-0 subset of E-1 subset of E-2 subset of center dot center dot center dot of function fields over F-q (with q = l(2)), where all extensions E-n/E-0 are Galois
Hardware implementation of a pipelined turbo decoder
Turbo codes have been widely studied since they were first proposed in 1993 by Berrou, Glavieux, and Thitimajshima in "Near Shannon Limit error-correcting coding and decoding: Turbo-codes" [1]. They have the advantage of providing a low bit error rate (BER) in decoding, and outperform linear block and convolutional codes in low signal-to-noise-ratio (SNR) environments. The decoding performance of turbo codes can be very close to the Shannon Limit, about 0.7decibel (dB). It is determined by the architectures of the constituent encoders and interleaver, but is bounded in high SNRs by an error floor. Turbo codes are widely used in communications. We explore the codeword weight spectrum properties that contribute to their excellent performance. Furthermore, the decoding performance is analyzed and compared with the free distance asymptotic performance. A 16-state turbo decoder is implemented using VHSIC Hardware Description Language (VHDL) and then mapped onto a field-programmable gate array (FPGA) board. The hardware implementations are compared with the software simulations to verify the decoding correctness. A pipelined architecture is then implemented which significantly reduces the decoding latency. -- Keywords: turbo codes; decoding performance; Monte Carlo simulations; FPGA implementatio
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
The Design of Efficiently-Encodable Rate-Compatible LDPC Codes
We present a new class of irregular low-density parity-check (LDPC) codes for
moderate block lengths (up to a few thousand bits) that are well-suited for
rate-compatible puncturing. The proposed codes show good performance under
puncturing over a wide range of rates and are suitable for usage in incremental
redundancy hybrid-automatic repeat request (ARQ) systems. In addition, these
codes are linear-time encodable with simple shift-register circuits. For a
block length of 1200 bits the codes outperform optimized irregular LDPC codes
and extended irregular repeat-accumulate (eIRA) codes for all puncturing rates
0.6~0.9 (base code performance is almost the same) and are particularly good at
high puncturing rates where good puncturing performance has been previously
difficult to achieve.Comment: Accepted subject to minor revision to IEEE Trans. on Com
CHANNEL CODING TECHNIQUES FOR A MULTIPLE TRACK DIGITAL MAGNETIC RECORDING SYSTEM
In magnetic recording greater area) bit packing densities are achieved through increasing
track density by reducing space between and width of the recording tracks, and/or
reducing the wavelength of the recorded information. This leads to the requirement of
higher precision tape transport mechanisms and dedicated coding circuitry.
A TMS320 10 digital signal processor is applied to a standard low-cost, low precision,
multiple-track, compact cassette tape recording system. Advanced signal processing and
coding techniques are employed to maximise recording density and to compensate for
the mechanical deficiencies of this system. Parallel software encoding/decoding
algorithms have been developed for several Run-Length Limited modulation codes. The
results for a peak detection system show that Bi-Phase L code can be reliably employed
up to a data rate of 5kbits/second/track. Development of a second system employing a
TMS32025 and sampling detection permitted the utilisation of adaptive equalisation to
slim the readback pulse. Application of conventional read equalisation techniques, that
oppose inter-symbol interference, resulted in a 30% increase in performance.
Further investigation shows that greater linear recording densities can be achieved by
employing Partial Response signalling and Maximum Likelihood Detection. Partial
response signalling schemes use controlled inter-symbol interference to increase
recording density at the expense of a multi-level read back waveform which results in an
increased noise penalty. Maximum Likelihood Sequence detection employs soft
decisions on the readback waveform to recover this loss. The associated modulation
coding techniques required for optimised operation of such a system are discussed.
Two-dimensional run-length-limited (d, ky) modulation codes provide a further means of
increasing storage capacity in multi-track recording systems. For example the code rate
of a single track run length-limited code with constraints (1, 3), such as Miller code, can
be increased by over 25% when using a 4-track two-dimensional code with the same d
constraint and with the k constraint satisfied across a number of parallel channels. The k
constraint along an individual track, kx, can be increased without loss of clock
synchronisation since the clocking information derived by frequent signal transitions
can be sub-divided across a number of, y, parallel tracks in terms of a ky constraint. This
permits more code words to be generated for a given (d, k) constraint in two dimensions
than is possible in one dimension. This coding technique is furthered by development of
a reverse enumeration scheme based on the trellis description of the (d, ky) constraints.
The application of a two-dimensional code to a high linear density system employing
extended class IV partial response signalling and maximum likelihood detection is
proposed. Finally, additional coding constraints to improve spectral response and error
performance are discussed.Hewlett Packard, Computer Peripherals Division (Bristol
Optimal Streaming Codes for Channels with Burst and Arbitrary Erasures
This paper considers transmitting a sequence of messages (a streaming source)
over a packet erasure channel. In each time slot, the source constructs a
packet based on the current and the previous messages and transmits the packet,
which may be erased when the packet travels from the source to the destination.
Every source message must be recovered perfectly at the destination subject to
a fixed decoding delay. We assume that the channel loss model introduces either
one burst erasure or multiple arbitrary erasures in any fixed-sized sliding
window. Under this channel loss assumption, we fully characterize the maximum
achievable rate by constructing streaming codes that achieve the optimal rate.
In addition, our construction of optimal streaming codes implies the full
characterization of the maximum achievable rate for convolutional codes with
any given column distance, column span and decoding delay. Numerical results
demonstrate that the optimal streaming codes outperform existing streaming
codes of comparable complexity over some instances of the Gilbert-Elliott
channel and the Fritchman channel.Comment: 36 pages, 3 figures, 2 table
The Telecommunications and Data Acquisition Report
This quarterly publication provides archival reports on developments in programs managed by JPL's Telecommunications and Mission Operations Directorate (TMOD), which now includes the former Telecommunications and Data Acquisition (TDA) Office. In space communications, radio navigation, radio science, and ground-based radio and radar astronomy, it reports on activities of the Deep Space Network (DSN) in planning, supporting research and technology, implementation, and operations. Also included are standards activity at JPL for space data and information systems and reimbursable DSN work performed for other space agencies through NASA. The preceding work is all performed for NASA's Office of Space Communications (OSC). TMOD also performs work funded by other NASA program offices through and with the cooperation of OSC. The first of these is the Orbital Debris Radar Program funded by the Office of Space Systems Development. It exists at Goldstone only and makes use of the planetary radar capability when the antennas are configured as science instruments making direct observations of the planets, their satellites, and asteroids of our solar system. The Office of Space Sciences funds the data reduction and science analyses of data obtained by the Goldstone Solar System Radar. The antennas at all three complexes are also configured for radio astronomy research and, as such, conduct experiments funded by the National Science Foundation in the U.S. and other agencies at the overseas complexes. These experiments are either in microwave spectroscopy or very long baseline interferometry. Finally, tasks funded under the JPL Director's Discretionary Fund and the Caltech President's Fund that involve TMOD are included. This and each succeeding issue of 'The Telecommunications and Data Acquisition Progress Report' will present material in some, but not necessarily all, of the aforementioned programs
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