730 research outputs found
Relaxed Polar Codes
Polar codes are the latest breakthrough in coding theory, as they are the
first family of codes with explicit construction that provably achieve the
symmetric capacity of discrete memoryless channels. Ar{\i}kan's polar encoder
and successive cancellation decoder have complexities of , for code
length . Although, the complexity bound of is asymptotically
favorable, we report in this work methods to further reduce the encoding and
decoding complexities of polar coding. The crux is to relax the polarization of
certain bit-channels without performance degradation. We consider schemes for
relaxing the polarization of both \emph{very good} and \emph{very bad}
bit-channels, in the process of channel polarization. Relaxed polar codes are
proved to preserve the capacity achieving property of polar codes. Analytical
bounds on the asymptotic and finite-length complexity reduction attainable by
relaxed polarization are derived.
For binary erasure channels, we show that the computation complexity can be
reduced by a factor of 6, while preserving the rate and error performance. We
also show that relaxed polar codes can be decoded with significantly reduced
latency. For AWGN channels with medium code lengths, we show that relaxed polar
codes can have lower error probabilities than conventional polar codes, while
having reduced encoding and decoding computation complexities.Comment: Conference version,Relaxed Channel Polarization for Reduced
Complexity Polar Coding, accepted for presentation at IEEE Wireless
Communications and Networking Conference WCNC 201
Reduced Complexity Belief Propagation Decoders for Polar Codes
Polar codes are newly discovered capacity-achieving codes, which have
attracted lots of research efforts. Polar codes can be efficiently decoded by
the low-complexity successive cancelation (SC) algorithm and the SC list (SCL)
decoding algorithm. The belief propagation (BP) decoding algorithm not only is
an alternative to the SC and SCL decoders, but also provides soft outputs that
are necessary for joint detection and decoding. Both the BP decoder and the
soft cancelation (SCAN) decoder were proposed for polar codes to output soft
information about the coded bits. In this paper, first a belief propagation
decoding algorithm, called reduced complexity soft cancelation (RCSC) decoding
algorithm, is proposed. Let denote the block length. Our RCSC decoding
algorithm needs to store only log-likelihood ratios (LLRs),
significantly less than and LLRs
needed by the BP and SCAN decoders, respectively, when .
Besides, compared to the SCAN decoding algorithm, our RCSC decoding algorithm
eliminates unnecessary additions over the real field. Then the simplified SC
(SSC) principle is applied to our RCSC decoding algorithm, and the resulting
SSC-aided RCSC (S-RCSC) decoding algorithm further reduces the computational
complexity. Finally, based on the S-RCSC decoding algorithm, we propose a
corresponding memory efficient decoder architecture, which has better error
performance than existing architectures. Besides, our decoder architecture
consumes less energy on updating LLRs.Comment: accepted by the IEEE 2015 workshop on signal processing systems
(SiPS
Low-complexity Recurrent Neural Network-based Polar Decoder with Weight Quantization Mechanism
Polar codes have drawn much attention and been adopted in 5G New Radio (NR)
due to their capacity-achieving performance. Recently, as the emerging deep
learning (DL) technique has breakthrough achievements in many fields, neural
network decoder was proposed to obtain faster convergence and better
performance than belief propagation (BP) decoding. However, neural networks are
memory-intensive and hinder the deployment of DL in communication systems. In
this work, a low-complexity recurrent neural network (RNN) polar decoder with
codebook-based weight quantization is proposed. Our test results show that we
can effectively reduce the memory overhead by 98% and alleviate computational
complexity with slight performance loss.Comment: 5 pages, accepted by the 2019 International Conference on Acoustics,
Speech, and Signal Processing (ICASSP
Data-Driven Ensembles for Deep and Hard-Decision Hybrid Decoding
Ensemble models are widely used to solve complex tasks by their decomposition
into multiple simpler tasks, each one solved locally by a single member of the
ensemble. Decoding of error-correction codes is a hard problem due to the curse
of dimensionality, leading one to consider ensembles-of-decoders as a possible
solution. Nonetheless, one must take complexity into account, especially in
decoding. We suggest a low-complexity scheme where a single member participates
in the decoding of each word. First, the distribution of feasible words is
partitioned into non-overlapping regions. Thereafter, specialized experts are
formed by independently training each member on a single region. A classical
hard-decision decoder (HDD) is employed to map every word to a single expert in
an injective manner. FER gains of up to 0.4dB at the waterfall region, and of
1.25dB at the error floor region are achieved for two BCH(63,36) and (63,45)
codes with cycle-reduced parity-check matrices, compared to the previous best
result of the paper "Active Deep Decoding of Linear Codes"
Deep Learning-based Polar Code Design
In this work, we introduce a deep learning-based polar code construction
algorithm. The core idea is to represent the information/frozen bit indices of
a polar code as a binary vector which can be interpreted as trainable weights
of a neural network (NN). For this, we demonstrate how this binary vector can
be relaxed to a soft-valued vector, facilitating the learning process through
gradient descent and enabling an efficient code construction. We further show
how different polar code design constraints (e.g., code rate) can be taken into
account by means of careful binary-to-soft and soft-to-binary conversions,
along with rate-adjustment after each learning iteration. Besides its
conceptual simplicity, this approach benefits from having the
"decoder-in-the-loop", i.e., the nature of the decoder is inherently taken into
consideration while learning (designing) the polar code. We show results for
belief propagation (BP) decoding over both AWGN and Rayleigh fading channels
with considerable performance gains over state-of-the-art construction schemes.Comment: Allerton201
Deep Unfolding for Communications Systems: A Survey and Some New Directions
Deep unfolding is a method of growing popularity that fuses iterative
optimization algorithms with tools from neural networks to efficiently solve a
range of tasks in machine learning, signal and image processing, and
communication systems. This survey summarizes the principle of deep unfolding
and discusses its recent use for communication systems with focus on detection
and precoding in multi-antenna (MIMO) wireless systems and belief propagation
decoding of error-correcting codes. To showcase the efficacy and generality of
deep unfolding, we describe a range of other tasks relevant to communication
systems that can be solved using this emerging paradigm. We conclude the survey
by outlining a list of open research problems and future research directions.Comment: IEEE Workshop on Signal Processing Systems (SiPS) 2019, special
session on "Practical Machine-Learning-Aided Communications Systems.
Syndrome-Enabled Unsupervised Learning for Neural Network-Based Polar Decoder and Jointly Optimized Blind Equalizer
Recently, the syndrome loss has been proposed to achieve "unsupervised
learning" for neural network-based BCH/LDPC decoders. However, the design
approach cannot be applied to polar codes directly and has not been evaluated
under varying channels. In this work, we propose two modified syndrome losses
to facilitate unsupervised learning in the receiver. Then, we first apply it to
a neural network-based belief propagation (BP) polar decoder. With the aid of
CRC-enabled syndrome loss, the BP decoder can even outperform conventional
supervised learning methods in terms of block error rate. Secondly, we propose
a jointly optimized syndrome-enabled blind equalizer, which can avoid the
transmission of training sequences and achieve global optimum with 1.3 dB gain
over non-blind minimum mean square error (MMSE) equalizer.Comment: 12 pages, 13 figures, 3 tables. Published in IEEE Journal on Emerging
and Selected Topics in Circuits and System
Representation-Oblivious Error Correction by Natural Redundancy
Storage systems have a strong need for substantially improving their error
correction capabilities, especially for long-term storage where the
accumulating errors can exceed the decoding threshold of error-correcting codes
(ECCs). In this work, a new scheme is presented that uses deep learning to
perform soft decoding for noisy files based on their natural redundancy. The
soft decoding result is then combined with ECCs for substantially better error
correction performance. The scheme is representation-oblivious: it requires no
prior knowledge on how data are represented (e.g., mapped from symbols to bits,
compressed, and combined with meta data) in different types of files, which
makes the solution more convenient to use for storage systems. Experimental
results confirm that the scheme can substantially improve the ability to
recover data for different types of files even when the bit error rates in the
files have significantly exceeded the decoding threshold of the ECC.Comment: 7 pages, 5 figures, submitted to IEEE International Conference on
Communications-201
Low-Latency SC Decoder Architectures for Polar Codes
Nowadays polar codes are becoming one of the most favorable capacity
achieving error correction codes for their low encoding and decoding
complexity. However, due to the large code length required by practical
applications, the few existing successive cancellation (SC) decoder
implementations still suffer from not only the high hardware cost but also the
long decoding latency. This paper presents novel several approaches to design
low-latency decoders for polar codes based on look-ahead techniques. Look-ahead
techniques can be employed to reschedule the decoding process of polar decoder
in numerous approaches. However, among those approaches, only well-arranged
ones can achieve good performance in terms of both latency and hardware
complexity. By revealing the recurrence property of SC decoding chart, the
authors succeed in reducing the decoding latency by 50% with look-ahead
techniques. With the help of VLSI-DSP design techniques such as pipelining,
folding, unfolding, and parallel processing, methodologies for four different
polar decoder architectures have been proposed to meet various application
demands. Sub-structure sharing scheme has been adopted to design the merged
processing element (PE) for further hardware reduction. In addition, systematic
methods for construction refined pipelining decoder (2nd design) and the input
generating circuits (ICG) block have been given. Detailed gate-level analysis
has demonstrated that the proposed designs show latency advantages over
conventional ones with similar hardware cost
perm2vec: Graph Permutation Selection for Decoding of Error Correction Codes using Self-Attention
Error correction codes are an integral part of communication applications,
boosting the reliability of transmission. The optimal decoding of transmitted
codewords is the maximum likelihood rule, which is NP-hard due to the curse of
dimensionality. For practical realizations, sub-optimal decoding algorithms are
employed; yet limited theoretical insights prevent one from exploiting the full
potential of these algorithms. One such insight is the choice of permutation in
permutation decoding. We present a data-driven framework for permutation
selection, combining domain knowledge with machine learning concepts such as
node embedding and self-attention. Significant and consistent improvements in
the bit error rate are introduced for all simulated codes, over the baseline
decoders. To the best of the authors' knowledge, this work is the first to
leverage the benefits of the neural Transformer networks in physical layer
communication systems
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