53,734 research outputs found
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
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
DeepTurbo: Deep Turbo Decoder
Present-day communication systems routinely use codes that approach the
channel capacity when coupled with a computationally efficient decoder.
However, the decoder is typically designed for the Gaussian noise channel and
is known to be sub-optimal for non-Gaussian noise distribution. Deep learning
methods offer a new approach for designing decoders that can be trained and
tailored for arbitrary channel statistics. We focus on Turbo codes and propose
DeepTurbo, a novel deep learning based architecture for Turbo decoding.
The standard Turbo decoder (Turbo) iteratively applies the
Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm with an interleaver in the middle. A
neural architecture for Turbo decoding termed (NeuralBCJR), was proposed
recently. There, the key idea is to create a module that imitates the BCJR
algorithm using supervised learning, and to use the interleaver architecture
along with this module, which is then fine-tuned using end-to-end training.
However, knowledge of the BCJR algorithm is required to design such an
architecture, which also constrains the resulting learned decoder. Here we
remedy this requirement and propose a fully end-to-end trained neural decoder -
Deep Turbo Decoder (DeepTurbo). With novel learnable decoder structure and
training methodology, DeepTurbo reveals superior performance under both AWGN
and non-AWGN settings as compared to the other two decoders - Turbo and
NeuralBCJR. Furthermore, among all the three, DeepTurbo exhibits the lowest
error floor
LEARN Codes: Inventing Low-latency Codes via Recurrent Neural Networks
Designing channel codes under low-latency constraints is one of the most
demanding requirements in 5G standards. However, a sharp characterization of
the performance of traditional codes is available only in the large
block-length limit. Guided by such asymptotic analysis, code designs require
large block lengths as well as latency to achieve the desired error rate.
Tail-biting convolutional codes and other recent state-of-the-art short block
codes, while promising reduced latency, are neither robust to channel-mismatch
nor adaptive to varying channel conditions. When the codes designed for one
channel (e.g.,~Additive White Gaussian Noise (AWGN) channel) are used for
another (e.g.,~non-AWGN channels), heuristics are necessary to achieve
non-trivial performance.
In this paper, we first propose an end-to-end learned neural code, obtained
by jointly designing a Recurrent Neural Network (RNN) based encoder and
decoder. This code outperforms canonical convolutional code under block
settings. We then leverage this experience to propose a new class of codes
under low-latency constraints, which we call Low-latency Efficient Adaptive
Robust Neural (LEARN) codes. These codes outperform state-of-the-art
low-latency codes and exhibit robustness and adaptivity properties. LEARN codes
show the potential to design new versatile and universal codes for future
communications via tools of modern deep learning coupled with communication
engineering insights
MIST: A Novel Training Strategy for Low-latency Scalable Neural Net Decoders
In this paper, we propose a low latency, robust and scalable neural net based
decoder for convolutional and low-density parity-check (LPDC) coding schemes.
The proposed decoders are demonstrated to have bit error rate (BER) and block
error rate (BLER) performances at par with the state-of-the-art neural net
based decoders while achieving more than 8 times higher decoding speed. The
enhanced decoding speed is due to the use of convolutional neural network (CNN)
as opposed to recurrent neural network (RNN) used in the best known neural net
based decoders. This contradicts existing doctrine that only RNN based decoders
can provide a performance close to the optimal ones. The key ingredient to our
approach is a novel Mixed-SNR Independent Samples based Training (MIST), which
allows for training of CNN with only 1\% of possible datawords, even for block
length as high as 1000. The proposed decoder is robust as, once trained, the
same decoder can be used for a wide range of SNR values. Finally, in the
presence of channel outages, the proposed decoders outperform the best known
decoders, {\it viz.} unquantized Viterbi decoder for convolutional code, and
belief propagation for LDPC. This gives the CNN decoder a significant advantage
in 5G millimeter wave systems, where channel outages are prevalent
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
Learning from the Syndrome
In this paper, we introduce the syndrome loss, an alternative loss function
for neural error-correcting decoders based on a relaxation of the syndrome. The
syndrome loss penalizes the decoder for producing outputs that do not
correspond to valid codewords. We show that training with the syndrome loss
yields decoders with consistently lower frame error rate for a number of short
block codes, at little additional cost during training and no additional cost
during inference. The proposed method does not depend on knowledge of the
transmitted codeword, making it a promising tool for online adaptation to
changing channel conditions.Comment: Accepted to Asilomar 2018 - special session on "Machine Learning for
Wireless Systems
Deep Learning Methods for Improved Decoding of Linear Codes
The problem of low complexity, close to optimal, channel decoding of linear
codes with short to moderate block length is considered. It is shown that deep
learning methods can be used to improve a standard belief propagation decoder,
despite the large example space. Similar improvements are obtained for the
min-sum algorithm. It is also shown that tying the parameters of the decoders
across iterations, so as to form a recurrent neural network architecture, can
be implemented with comparable results. The advantage is that significantly
less parameters are required. We also introduce a recurrent neural decoder
architecture based on the method of successive relaxation. Improvements over
standard belief propagation are also observed on sparser Tanner graph
representations of the codes. Furthermore, we demonstrate that the neural
belief propagation decoder can be used to improve the performance, or
alternatively reduce the computational complexity, of a close to optimal
decoder of short BCH codes.Comment: Accepted To IEEE Journal Of Selected Topics In Signal Processin
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
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