278 research outputs found
Pipelined Architecture for Soft-decision Iterative Projection Aggregation Decoding for RM Codes
The recently proposed recursive projection-aggregation (RPA) decoding
algorithm for Reed-Muller codes has received significant attention as it
provides near-ML decoding performance at reasonable complexity for short codes.
However, its complicated structure makes it unsuitable for hardware
implementation. Iterative projection-aggregation (IPA) decoding is a modified
version of RPA decoding that simplifies the hardware implementation. In this
work, we present a flexible hardware architecture for the IPA decoder that can
be configured from fully-sequential to fully-parallel, thus making it suitable
for a wide range of applications with different constraints and resource
budgets. Our simulation and implementation results show that the IPA decoder
has 41% lower area consumption, 44% lower latency, four times higher
throughput, but currently seven times higher power consumption for a code with
block length of 128 and information length of 29 compared to a state-of-the-art
polar successive cancellation list (SCL) decoder with comparable decoding
performance
Semi-Deterministic Subspace Selection for Sparse Recursive Projection-Aggregation Decoding of Reed-Muller Codes
Recursive projection aggregation (RPA) decoding as introduced in [1] is a
novel decoding algorithm which performs close to the maximum likelihood decoder
for short-length Reed-Muller codes. Recently, an extension to RPA decoding,
called sparse multi-decoder RPA (SRPA), has been proposed [2]. The SRPA
approach makes use of multiple pruned RPA decoders to lower the amount of
computations while keeping the performance loss small compared to RPA decoding.
However, the use of multiple sparse decoders again increases the computational
burden. Therefore, the focus is on the optimization of sparse single-decoder
RPA decoding to keep the complexity small. In this paper, a novel method is
proposed, to select subsets of subspaces used in the projection and aggregation
step of SRPA decoding in order to decrease the decoding error probability on
AWGN channels. The proposed method replaces the random selection of subspace
subsets with a semi-deterministic selection method based on a figure of merit
that evaluates the performance of each subspace. Our simulation results show
that the semi-deterministic subspace selection improves the decoding
performance up to compared to SRPA. At the same time, the
complexity of SRPA decoding for RM codes of order is reduced by up to
81% compared to SRPA
Sparse Multi-Decoder Recursive Projection Aggregation for Reed-Muller Codes
Reed-Muller (RM) codes are one of the oldest families of codes. Recently, a
recursive projection aggregation (RPA) decoder has been proposed, which
achieves a performance that is close to the maximum likelihood decoder for
short-length RM codes. One of its main drawbacks, however, is the large amount
of computations needed. In this paper, we devise a new algorithm to lower the
computational budget while keeping a performance close to that of the RPA
decoder. The proposed approach consists of multiple sparse RPAs that are
generated by performing only a selection of projections in each sparsified
decoder. In the end, a cyclic redundancy check (CRC) is used to decide between
output codewords. Simulation results show that our proposed approach reduces
the RPA decoder's computations up to with negligible performance loss.Comment: 6 pages, 12 figure
Turbo codes and turbo algorithms
In the first part of this paper, several basic ideas that prompted the coming of turbo codes are commented on. We then present some personal points of view on the main advances obtained in past years on turbo coding and decoding such as the circular trellis termination of recursive systematic convolutional codes and double-binary turbo codes associated with Max-Log-MAP decoding. A novel evaluation method, called genieinitialised iterative processing (GIIP), is introduced to assess the error performance of iterative processing. We show that using GIIP produces a result that can be viewed as a lower bound of the maximum likelihood iterative decoding and detection performance. Finally, two wireless communication systems are presented to illustrate recent applications of the turbo principle, the first one being multiple-input/multiple-output channel iterative detection and the second one multi-carrier modulation with linear precoding
Hardware Implementation of Iterative Projection-Aggregation Decoding of Reed-Muller Codes
In this work, we present a simplification and a corresponding hardware
architecture for hard-decision recursive projection-aggregation (RPA) decoding
of Reed-Muller (RM) codes. In particular, we transform the recursive structure
of RPA decoding into a simpler and iterative structure with minimal
error-correction degradation. Our simulation results for RM(7,3) show that the
proposed simplification has a small error-correcting performance degradation
(0.005 in terms of channel crossover probability) while reducing the average
number of computations by up to 40%. In addition, we describe the first fully
parallel hardware architecture for simplified RPA decoding. We present FPGA
implementation results for an RM(6,3) code on a Xilinx Virtex-7 FPGA showing
that our proposed architecture achieves a throughput of 171 Mbps at a frequency
of 80 MHz
Multi-Factor Pruning for Recursive Projection-Aggregation Decoding of RM Codes
The recently introduced recursive projection aggregation (RPA) decoding
method for Reed-Muller (RM) codes can achieve near-maximum likelihood (ML)
decoding performance. However, its high computational complexity makes its
implementation challenging for time- and resource-critical applications. In
this work, we present a complexity reduction technique called multi-factor
pruning that reduces the computational complexity of RPA significantly. Our
simulation results show that the proposed pruning approach with appropriately
selected factors can reduce the complexity of RPA by up to for
while keeping the comparable error-correcting performance
Study of information transfer optimization for communication satellites
The results are presented of a study of source coding, modulation/channel coding, and systems techniques for application to teleconferencing over high data rate digital communication satellite links. Simultaneous transmission of video, voice, data, and/or graphics is possible in various teleconferencing modes and one-way, two-way, and broadcast modes are considered. A satellite channel model including filters, limiter, a TWT, detectors, and an optimized equalizer is treated in detail. A complete analysis is presented for one set of system assumptions which exclude nonlinear gain and phase distortion in the TWT. Modulation, demodulation, and channel coding are considered, based on an additive white Gaussian noise channel model which is an idealization of an equalized channel. Source coding with emphasis on video data compression is reviewed, and the experimental facility utilized to test promising techniques is fully described
The Road From Classical to Quantum Codes: A Hashing Bound Approaching Design Procedure
Powerful Quantum Error Correction Codes (QECCs) are required for stabilizing
and protecting fragile qubits against the undesirable effects of quantum
decoherence. Similar to classical codes, hashing bound approaching QECCs may be
designed by exploiting a concatenated code structure, which invokes iterative
decoding. Therefore, in this paper we provide an extensive step-by-step
tutorial for designing EXtrinsic Information Transfer (EXIT) chart aided
concatenated quantum codes based on the underlying quantum-to-classical
isomorphism. These design lessons are then exemplified in the context of our
proposed Quantum Irregular Convolutional Code (QIRCC), which constitutes the
outer component of a concatenated quantum code. The proposed QIRCC can be
dynamically adapted to match any given inner code using EXIT charts, hence
achieving a performance close to the hashing bound. It is demonstrated that our
QIRCC-based optimized design is capable of operating within 0.4 dB of the noise
limit
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