11 research outputs found
Braided Convolutional Codes with Sliding Window Decoding
In this paper, we present a novel sliding window decoding scheme based on iterative Bahl-Cocke-Jelinek-Raviv decoding for braided convolutional codes, a class of turbo-like codes with short constraint length component convolutional codes. The tradeoff between performance and decoding latency is examined and, to reduce decoding complexity, both uniform and nonuniform message passing schedules within the decoding window, along with early stopping rules, are proposed. We also perform a density evolution analysis of sliding window decoding to guide the selection of the window size and message passing schedule. Periodic puncturing is employed to obtain rate-compatible code rates of 1/2 and 2/3 starting from a rate 1/3 mother code and a code rate of 3/4 starting from a rate 1/2 mother code. Simulation results show that, with nonuniform message passing and periodic puncturing, near capacity performance can be maintained throughout a wide range of rates with reasonable decoding complexity and no visible error floors
Thresholds of Braided Convolutional Codes on the AWGN Channel
In this paper, we perform a threshold analysis of braided convolutional codes
(BCCs) on the additive white Gaussian noise (AWGN) channel. The decoding
thresholds are estimated by Monte-Carlo density evolution (MC-DE) techniques
and compared with approximate thresholds from an erasure channel prediction.
The results show that, with spatial coupling, the predicted thresholds are very
accurate and quickly approach capacity if the coupling memory is increased. For
uncoupled ensembles with random puncturing, the prediction can be improved with
help of the AWGN threshold of the unpunctured ensemble
Combating Error Propagation in Window Decoding of Braided Convolutional Codes
In this paper, we study sliding window decoding of braided convolutional
codes (BCCs) in the context of a streaming application, where decoder error
propagation can be a serious problem. A window extension algorithm and a
resynchronization mechanism are introduced to mitigate the effect of error
propagation. In addition, we introduce a soft bit-error-rate stopping rule to
reduce computational complexity, and the tradeoff between performance and
complexity is examined. Simulation results show that, using the proposed window
extension algorithm and resynchronization mechanism, the error performance of
BCCs can be improved by up to three orders of magnitude with reduced
computational complexity.Comment: 6 pages, 10 figures, submitted for IEEE ISIT201
Spatial Coupling in Turbo Equalization
In this paper we consider spatial coupling in turbo equalization and demonstrate that the code design trade-off between the performance in waterfall and error floor regions can be avoided. We introduce three coupling schemes and compare their performances, where the first method introduces coupling between the encoder and the channel, while the second uses a spatially coupled (SC) code. In the third scheme we use both a coupled code and couple between the code and the channel. We show by computer simulations that, with spatial coupling, we can have good performance in both the error floor and the waterfall region with reasonable decoding latency by using a window decoder. We show this for both the maximum a posteriori (MAP) and linear minimum mean square (MMSE) equalizers
Error Propagation Mitigation in Sliding Window Decoding of Braided Convolutional Codes
We investigate error propagation in sliding window decoding of braided
convolutional codes (BCCs). Previous studies of BCCs have focused on iterative
decoding thresholds, minimum distance properties, and their bit error rate
(BER) performance at small to moderate frame length. Here, we consider a
sliding window decoder in the context of large frame length or one that
continuously outputs blocks in a streaming fashion. In this case, decoder error
propagation, due to the feedback inherent in BCCs, can be a serious problem.In
order to mitigate the effects of error propagation, we propose several schemes:
a \emph{window extension algorithm} where the decoder window size can be
extended adaptively, a resynchronization mechanism where we reset the encoder
to the initial state, and a retransmission strategy where erroneously decoded
blocks are retransmitted. In addition, we introduce a soft BER stopping rule to
reduce computational complexity, and the tradeoff between performance and
complexity is examined. Simulation results show that, using the proposed window
extension algorithm, resynchronization mechanism, and retransmission strategy,
the BER performance of BCCs can be improved by up to four orders of magnitude
in the signal-to-noise ratio operating range of interest, and in addition the
soft BER stopping rule can be employed to reduce computational complexity.Comment: arXiv admin note: text overlap with arXiv:1801.0323
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
Generalized Spatially-Coupled Parallel Concatenated Codes With Partial Repetition
A new class of spatially-coupled turbo-like codes (SC-TCs), dubbed generalized spatially coupled parallel concatenated codes (GSC-PCCs), is introduced. These codes are constructed by applying spatial coupling on parallel concatenated codes (PCCs) with a fraction of information bits repeated q times. GSC-PCCs can be seen as a generalization of the original spatially-coupled parallel concatenated codes proposed by Moloudi et al. [2]. To characterize the asymptotic performance of GSC-PCCs, we derive the corresponding density evolution equations and compute their decoding thresholds. The threshold saturation effect is observed and proven. Most importantly, we rigorously prove that the rate-R GSC-PCC ensemble with 2-state convolutional component codes achieves at least a fraction 1-R/R+q of the capacity of the binary erasure channel (BEC) for repetition factor q ≥ 2 and this multiplicative gap vanishes as q tends to infinity. To the best of our knowledge, this is the first class of SC-TCs that are proven to be capacity-achieving. Further, the connection between the strength of the component codes, the decoding thresholds of GSC-PCCs, and the repetition factor is established. The superiority of the proposed codes with finite blocklength is exemplified by comparing their error performance with that of existing SC-TCs via computer simulations
Spatially-Coupled QDLPC Codes
Spatially-coupled (SC) codes is a class of convolutional LDPC codes that has
been well investigated in classical coding theory thanks to their high
performance and compatibility with low-latency decoders. We describe toric
codes as quantum counterparts of classical two-dimensional spatially-coupled
(2D-SC) codes, and introduce spatially-coupled quantum LDPC (SC-QLDPC) codes as
a generalization. We use the convolutional structure to represent the parity
check matrix of a 2D-SC code as a polynomial in two indeterminates, and derive
an algebraic condition that is both necessary and sufficient for a 2D-SC code
to be a stabilizer code. This algebraic framework facilitates the construction
of new code families. While not the focus of this paper, we note that small
memory facilitates physical connectivity of qubits, and it enables local
encoding and low-latency windowed decoding. In this paper, we use the algebraic
framework to optimize short cycles in the Tanner graph of 2D-SC HGP codes that
arise from short cycles in either component code. While prior work focuses on
QLDPC codes with rate less than 1/10, we construct 2D-SC HGP codes with small
memory, higher rates (about 1/3), and superior thresholds.Comment: 25 pages, 7 figure