661 research outputs found
A Gated Hypernet Decoder for Polar Codes
Hypernetworks were recently shown to improve the performance of message
passing algorithms for decoding error correcting codes. In this work, we
demonstrate how hypernetworks can be applied to decode polar codes by employing
a new formalization of the polar belief propagation decoding scheme. We
demonstrate that our method improves the previous results of neural polar
decoders and achieves, for large SNRs, the same bit-error-rate performances as
the successive list cancellation method, which is known to be better than any
belief propagation decoders and very close to the maximum likelihood decoder.Comment: Accepted to ICASSP 202
Learning to Denoise and Decode: A Novel Residual Neural Network Decoder for Polar Codes
Polar codes have been adopted as the control channel coding scheme in the
fifth generation new radio (5G NR) standard due to its capacity-achievable
property. Traditional polar decoding algorithms such as successive cancellation
(SC) suffer from high latency problem because of their sequential decoding
nature. Neural network decoder (NND) has been proved to be a candidate for
polar decoder since it is capable of oneshot decoding and parallel computing.
Whereas, the bit-errorrate (BER) performance of NND is still inferior to that
of SC algorithm. In this paper, we propose a residual neural network decoder
(RNND) for polar codes. Different from previous works which directly use neural
network for decoding symbols received from the channel, the proposed RNND
introduces a denoising module based on residual learning before NND. The
proposed residual learning denoiser is able to remove remarkable amount of
noise from received signals. Numerical results show that our proposed RNND
outperforms traditional NND with regard to the BER performance under comparable
latency.Comment: 6 pages, 9 figure
Convolutional Neural Network-aided Bit-flipping for Belief Propagation Decoding of Polar Codes
Known for their capacity-achieving abilities, polar codes have been selected
as the control channel coding scheme for 5G communications. To satisfy the
needs of high throughput and low latency, belief propagation (BP) is chosen as
the decoding algorithm. However, in general, the error performance of BP is
worse than that of enhanced successive cancellation (SC). Recently,
critical-set bit-flipping (CS-BF) is applied to BP decoding to lower the error
rate. However, its trial and error process result in even longer latency. In
this work, we propose a convolutional neural network-assisted bit-flipping
(CNN-BF) mechanism to further enhance BP decoding of polar codes. With
carefully designed input data and model architecture, our proposed CNN-BF can
achieve much higher prediction accuracy and better error correction capability
than CS-BF but with only half latency. It also achieves a lower block error
rate (BLER) than SC list (CA-SCL).Comment: 5 pages, 6 figure
Learning to Flip Successive Cancellation Decoding of Polar Codes with LSTM Networks
The key to successive cancellation (SC) flip decoding of polar codes is to
accurately identify the first error bit. The optimal flipping strategy is
considered difficult due to lack of an analytical solution. Alternatively, we
propose a deep learning aided SC flip algorithm. Specifically, before each SC
decoding attempt, a long short-term memory (LSTM) network is exploited to
either (i) locate the first error bit, or (ii) undo a previous `wrong' flip. In
each SC attempt, the sequence of log likelihood ratios (LLRs) derived in the
previous SC attempt is exploited to decide which action to take. Accordingly, a
two-stage training method of the LSTM network is proposed, i.e., learn to
locate first error bits in the first stage, and then to undo `wrong' flips in
the second stage. Simulation results show that the proposed approach identifies
error bits more accurately and achieves better performance than the
state-of-the-art SC flip algorithms.Comment: 5 pages, 7 figure
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
DNC-Aided SCL-Flip Decoding of Polar Codes
Successive-cancellation list (SCL) decoding of polar codes has been adopted
for 5G. However, the performance is not very satisfactory with moderate code
length. Heuristic or deep-learning-aided (DL-aided) flip algorithms have been
developed to tackle this problem. The key for successful flip decoding is to
accurately identify error bit positions. In this work, we propose a new flip
algorithm with help of differentiable neural computer (DNC). New state and
action encoding are developed for better DNC training and inference efficiency.
The proposed method consists of two phases: i) a flip DNC (F-DNC) is exploited
to rank most likely flip positions for multi-bit flipping; ii) if decoding
still fails, a flip-validate DNC (FV-DNC) is used to re-select error bit
positions for successive flip decoding trials. Supervised training methods are
designed accordingly for the two DNCs. Simulation results show that proposed
DNC-aided SCL-Flip (DNC-SCLF) decoding demonstrates up to 0.34dB coding gain
improvement or 54.2 reduction in average number of decoding attempts compared
to prior works.Comment: Submitted to Globecom 202
Convolutional Polar Codes
Arikan's Polar codes attracted much attention as the first efficiently
decodable and capacity achieving codes. Furthermore, Polar codes exhibit an
exponentially decreasing block error probability with an asymptotic error
exponent upper bounded by 1/2. Since their discovery, many attempts have been
made to improve the error exponent and the finite block-length performance,
while keeping the bloc-structured kernel. Recently, two of us introduced a new
family of efficiently decodable error-correction codes based on a recently
discovered efficiently-contractible tensor network family in quantum many-body
physics, called branching MERA. These codes, called branching MERA codes,
include Polar codes and also extend them in a non-trivial way by substituting
the bloc-structured kernel by a convolutional structure. Here, we perform an
in-depth study of a particular example that can be thought of as a direct
extension to Arikan's Polar code, which we therefore name Convolutional Polar
codes. We prove that these codes polarize and exponentially suppress the
channel's error probability, with an asymptotic error exponent log_2(3)/2 which
is provably better than for Polar codes under successive cancellation decoding.
We also perform finite block-size numerical simulations which display improved
error-correcting capability with only a minor impact on decoding complexity.Comment: Subsumes arXiv:1312.457
Reinforcement Learning for Nested Polar Code Construction
In this paper, we model nested polar code construction as a Markov decision
process (MDP), and tackle it with advanced reinforcement learning (RL)
techniques. First, an MDP environment with state, action, and reward is defined
in the context of polar coding. Specifically, a state represents the
construction of an polar code, an action specifies its reduction to an
subcode, and reward is the decoding performance. A neural network
architecture consisting of both policy and value networks is proposed to
generate actions based on the observed states, aiming at maximizing the overall
rewards. A loss function is defined to trade off between exploitation and
exploration. To further improve learning efficiency and quality, an `integrated
learning' paradigm is proposed. It first employs a genetic algorithm to
generate a population of (sub-)optimal polar codes for each , and then
uses them as prior knowledge to refine the policy in RL. Such a paradigm is
shown to accelerate the training process, and converge at better performances.
Simulation results show that the proposed learning-based polar constructions
achieve comparable, or even better, performances than the state of the art
under successive cancellation list (SCL) decoders. Last but not least, this is
achieved without exploiting any expert knowledge from polar coding theory in
the learning algorithms.Comment: 8 pages, 10 figures, propose a multi-stage genetic algorith
Neural Belief Propagation Decoding of CRC-Polar Concatenated Codes
Polar codes are the first class of error correcting codes that provably
achieve the channel capacity at infinite code length. They were selected for
use in the fifth generation of cellular mobile communications (5G). In
practical scenarios such as 5G, a cyclic redundancy check (CRC) is concatenated
with polar codes to improve their finite length performance. This is mostly
beneficial for sequential successive-cancellation list decoders. However, for
parallel iterative belief propagation (BP) decoders, CRC is only used as an
early stopping criterion with incremental error-correction performance
improvement. In this paper, we first propose a CRC-polar BP (CPBP) decoder by
exchanging the extrinsic information between the factor graph of the polar code
and that of the CRC. We then propose a neural CPBP (NCPBP) algorithm which
improves the CPBP decoder by introducing trainable normalizing weights on the
concatenated factor graph. Our results on a 5G polar code of length 128 show
that at the frame error rate of 10^(-5) and with a maximum of 30 iterations,
the error-correction performance of CPBP and NCPBP are approximately 0.25 dB
and 0.5 dB better than that of the conventional CRC-aided BP decoder,
respectively, while introducing almost no latency overhead
Low-Complexity LSTM-Assisted Bit-Flipping Algorithm for Successive Cancellation List Polar Decoder
Polar codes have attracted much attention in the past decade due to their
capacity-achieving performance. The higher decoding capacity is required for 5G
and beyond 5G (B5G). Although the cyclic redundancy check (CRC)- assisted
successive cancellation list bit-flipping (CA-SCLF) decoders have been
developed to obtain a better performance, the solution to error bit correction
(bit-flipping) problem is still imperfect and hard to design. In this work, we
leverage the expert knowledge in communication systems and adopt deep learning
(DL) technique to obtain the better solution. A low-complexity long short-term
memory network (LSTM)-assisted CA-SCLF decoder is proposed to further improve
the performance of conventional CA-SCLF and avoid complexity and memory
overhead. Our test results show that we can effectively improve the BLER
performance by 0.11dB compared to prior work and reduce the complexity and
memory overhead by over 30% of the network.Comment: 5 pages, 5 figure
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