426 research outputs found
Iterative Decoding and Turbo Equalization: The Z-Crease Phenomenon
Iterative probabilistic inference, popularly dubbed the soft-iterative
paradigm, has found great use in a wide range of communication applications,
including turbo decoding and turbo equalization. The classic approach of
analyzing the iterative approach inevitably use the statistical and
information-theoretical tools that bear ensemble-average flavors. This paper
consider the per-block error rate performance, and analyzes it using nonlinear
dynamical theory. By modeling the iterative processor as a nonlinear dynamical
system, we report a universal "Z-crease phenomenon:" the zig-zag or up-and-down
fluctuation -- rather than the monotonic decrease -- of the per-block errors,
as the number of iteration increases. Using the turbo decoder as an example, we
also report several interesting motion phenomenons which were not previously
reported, and which appear to correspond well with the notion of "pseudo
codewords" and "stopping/trapping sets." We further propose a heuristic
stopping criterion to control Z-crease and identify the best iteration. Our
stopping criterion is most useful for controlling the worst-case per-block
errors, and helps to significantly reduce the average-iteration numbers.Comment: 6 page
Information-Coupled Turbo Codes for LTE Systems
We propose a new class of information-coupled (IC) Turbo codes to improve the
transport block (TB) error rate performance for long-term evolution (LTE)
systems, while keeping the hybrid automatic repeat request protocol and the
Turbo decoder for each code block (CB) unchanged. In the proposed codes, every
two consecutive CBs in a TB are coupled together by sharing a few common
information bits. We propose a feed-forward and feed-back decoding scheme and a
windowed (WD) decoding scheme for decoding the whole TB by exploiting the
coupled information between CBs. Both decoding schemes achieve a considerable
signal-to-noise-ratio (SNR) gain compared to the LTE Turbo codes. We construct
the extrinsic information transfer (EXIT) functions for the LTE Turbo codes and
our proposed IC Turbo codes from the EXIT functions of underlying convolutional
codes. An SNR gain upper bound of our proposed codes over the LTE Turbo codes
is derived and calculated by the constructed EXIT charts. Numerical results
show that the proposed codes achieve an SNR gain of 0.25 dB to 0.72 dB for
various code parameters at a TB error rate level of , which complies
with the derived SNR gain upper bound.Comment: 13 pages, 12 figure
Distributed Turbo-Like Codes for Multi-User Cooperative Relay Networks
In this paper, a distributed turbo-like coding scheme for wireless networks
with relays is proposed. We consider a scenario where multiple sources
communicate with a single destination with the help of a relay. The proposed
scheme can be regarded as of the decode-and-forward type. The relay decodes the
information from the sources and it properly combines and re-encodes them to
generate some extra redundancy, which is transmitted to the destination. The
amount of redundancy generated by the relay can simply be adjusted according to
requirements in terms of performance, throughput and/or power. At the
destination, decoding of the information of all sources is performed jointly
exploiting the redundancy provided by the relay in an iterative fashion. The
overall communication network can be viewed as a serially concatenated code.
The proposed distributed scheme achieves significant performance gains with
respect to the non-cooperation system, even for a very large number of users.
Furthermore, it presents a high flexibility in terms of code rate, block length
and number of users.Comment: Submitted to ICC 201
Discriminated Belief Propagation
Near optimal decoding of good error control codes is generally a difficult
task. However, for a certain type of (sufficiently) good codes an efficient
decoding algorithm with near optimal performance exists. These codes are
defined via a combination of constituent codes with low complexity trellis
representations. Their decoding algorithm is an instance of (loopy) belief
propagation and is based on an iterative transfer of constituent beliefs. The
beliefs are thereby given by the symbol probabilities computed in the
constituent trellises. Even though weak constituent codes are employed close to
optimal performance is obtained, i.e., the encoder/decoder pair (almost)
achieves the information theoretic capacity. However, (loopy) belief
propagation only performs well for a rather specific set of codes, which limits
its applicability.
In this paper a generalisation of iterative decoding is presented. It is
proposed to transfer more values than just the constituent beliefs. This is
achieved by the transfer of beliefs obtained by independently investigating
parts of the code space. This leads to the concept of discriminators, which are
used to improve the decoder resolution within certain areas and defines
discriminated symbol beliefs. It is shown that these beliefs approximate the
overall symbol probabilities. This leads to an iteration rule that (below
channel capacity) typically only admits the solution of the overall decoding
problem. Via a Gauss approximation a low complexity version of this algorithm
is derived. Moreover, the approach may then be applied to a wide range of
channel maps without significant complexity increase
Soft-Decoding-Based Strategies for Relay and Interference Channels: Analysis and Achievable Rates Using LDPC Codes
We provide a rigorous mathematical analysis of two communication strategies:
soft decode-and-forward (soft-DF) for relay channels, and soft partial
interference-cancelation (soft-IC) for interference channels. Both strategies
involve soft estimation, which assists the decoding process. We consider LDPC
codes, not because of their practical benefits, but because of their analytic
tractability, which enables an asymptotic analysis similar to random coding
methods of information theory. Unlike some works on the closely-related
demodulate-and-forward, we assume non-memoryless, code-structure-aware
estimation. With soft-DF, we develop {\it simultaneous density evolution} to
bound the decoding error probability at the destination. This result applies to
erasure relay channels. In one variant of soft-DF, the relay applies Wyner-Ziv
coding to enhance its communication with the destination, borrowing from
compress-and-forward. To analyze soft-IC, we adapt existing techniques for
iterative multiuser detection, and focus on binary-input additive white
Gaussian noise (BIAWGN) interference channels. We prove that optimal
point-to-point codes are unsuitable for soft-IC, as well as for all strategies
that apply partial decoding to improve upon single-user detection (SUD) and
multiuser detection (MUD), including Han-Kobayashi (HK).Comment: Accepted to the IEEE Transactions on Information Theory. This is a
major revision of a paper originally submitted in August 201
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