12,468 research outputs found
Short Block-length Codes for Ultra-Reliable Low-Latency Communications
This paper reviews the state of the art channel coding techniques for
ultra-reliable low latency communication (URLLC). The stringent requirements of
URLLC services, such as ultra-high reliability and low latency, have made it
the most challenging feature of the fifth generation (5G) mobile systems. The
problem is even more challenging for the services beyond the 5G promise, such
as tele-surgery and factory automation, which require latencies less than 1ms
and failure rate as low as . The very low latency requirements of
URLLC do not allow traditional approaches such as re-transmission to be used to
increase the reliability. On the other hand, to guarantee the delay
requirements, the block length needs to be small, so conventional channel
codes, originally designed and optimised for moderate-to-long block-lengths,
show notable deficiencies for short blocks. This paper provides an overview on
channel coding techniques for short block lengths and compares them in terms of
performance and complexity. Several important research directions are
identified and discussed in more detail with several possible solutions.Comment: Accepted for publication in IEEE Communications Magazin
Entanglement-assisted quantum turbo codes
An unexpected breakdown in the existing theory of quantum serial turbo coding
is that a quantum convolutional encoder cannot simultaneously be recursive and
non-catastrophic. These properties are essential for quantum turbo code
families to have a minimum distance growing with blocklength and for their
iterative decoding algorithm to converge, respectively. Here, we show that the
entanglement-assisted paradigm simplifies the theory of quantum turbo codes, in
the sense that an entanglement-assisted quantum (EAQ) convolutional encoder can
possess both of the aforementioned desirable properties. We give several
examples of EAQ convolutional encoders that are both recursive and
non-catastrophic and detail their relevant parameters. We then modify the
quantum turbo decoding algorithm of Poulin et al., in order to have the
constituent decoders pass along only "extrinsic information" to each other
rather than a posteriori probabilities as in the decoder of Poulin et al., and
this leads to a significant improvement in the performance of unassisted
quantum turbo codes. Other simulation results indicate that
entanglement-assisted turbo codes can operate reliably in a noise regime 4.73
dB beyond that of standard quantum turbo codes, when used on a memoryless
depolarizing channel. Furthermore, several of our quantum turbo codes are
within 1 dB or less of their hashing limits, so that the performance of quantum
turbo codes is now on par with that of classical turbo codes. Finally, we prove
that entanglement is the resource that enables a convolutional encoder to be
both non-catastrophic and recursive because an encoder acting on only
information qubits, classical bits, gauge qubits, and ancilla qubits cannot
simultaneously satisfy them.Comment: 31 pages, software for simulating EA turbo codes is available at
http://code.google.com/p/ea-turbo/ and a presentation is available at
http://markwilde.com/publications/10-10-EA-Turbo.ppt ; v2, revisions based on
feedback from journal; v3, modification of the quantum turbo decoding
algorithm that leads to improved performance over results in v2 and the
results of Poulin et al. in arXiv:0712.288
Localized Dimension Growth in Random Network Coding: A Convolutional Approach
We propose an efficient Adaptive Random Convolutional Network Coding (ARCNC)
algorithm to address the issue of field size in random network coding. ARCNC
operates as a convolutional code, with the coefficients of local encoding
kernels chosen randomly over a small finite field. The lengths of local
encoding kernels increase with time until the global encoding kernel matrices
at related sink nodes all have full rank. Instead of estimating the necessary
field size a priori, ARCNC operates in a small finite field. It adapts to
unknown network topologies without prior knowledge, by locally incrementing the
dimensionality of the convolutional code. Because convolutional codes of
different constraint lengths can coexist in different portions of the network,
reductions in decoding delay and memory overheads can be achieved with ARCNC.
We show through analysis that this method performs no worse than random linear
network codes in general networks, and can provide significant gains in terms
of average decoding delay in combination networks.Comment: 7 pages, 1 figure, submitted to IEEE ISIT 201
Channels with block interference
A new class of channel models with memory is presented in order to study various kinds of interference phenomena. It is shown, among other things, that when all other parameters are held fixed, channel capacity C is an increasing function of the memory length, while the cutoff rate R0 generally is a decreasing function. Calculations with various explicit coding schemes indicate that C is better than R0 as a performance measure for these channel models. As a partial resolution of this C versus R0 paradox, the conjecture is offered that R0 is more properly a measure of coding delay rather than of coding complexity
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