580 research outputs found
Threshold-Based Fast Successive-Cancellation Decoding of Polar Codes
Fast SC decoding overcomes the latency caused by the serial nature of the SC
decoding by identifying new nodes in the upper levels of the SC decoding tree
and implementing their fast parallel decoders. In this work, we first present a
novel sequence repetition node corresponding to a particular class of bit
sequences. Most existing special node types are special cases of the proposed
sequence repetition node. Then, a fast parallel decoder is proposed for this
class of node. To further speed up the decoding process of general nodes
outside this class, a threshold-based hard-decision-aided scheme is introduced.
The threshold value that guarantees a given error-correction performance in the
proposed scheme is derived theoretically. Analysis and hardware implementation
results on a polar code of length with code rates , , and
show that our proposed algorithm reduces the required clock cycles by up
to , and leads to a improvement in the maximum operating frequency
compared to state-of-the-art decoders without tangibly altering the
error-correction performance. In addition, using the proposed threshold-based
hard-decision-aided scheme, the decoding latency can be further reduced by
at ~dB.Comment: 14 pages, 8 figures, 5 tables, submitted to IEEE Transactions on
Communication
Partitioned List Decoding of Polar Codes: Analysis and Improvement of Finite Length Performance
Polar codes represent one of the major recent breakthroughs in coding theory
and, because of their attractive features, they have been selected for the
incoming 5G standard. As such, a lot of attention has been devoted to the
development of decoding algorithms with good error performance and efficient
hardware implementation. One of the leading candidates in this regard is
represented by successive-cancellation list (SCL) decoding. However, its
hardware implementation requires a large amount of memory. Recently, a
partitioned SCL (PSCL) decoder has been proposed to significantly reduce the
memory consumption. In this paper, we examine the paradigm of PSCL decoding
from both theoretical and practical standpoints: (i) by changing the
construction of the code, we are able to improve the performance at no
additional computational, latency or memory cost, (ii) we present an optimal
scheme to allocate cyclic redundancy checks (CRCs), and (iii) we provide an
upper bound on the list size that allows MAP performance.Comment: 2017 IEEE Global Communications Conference (GLOBECOM
List Autoencoder: Towards Deep Learning Based Reliable Transmission Over Noisy Channels
In this paper, we present list autoencoder (listAE) to mimic list decoding
used in classical coding theory. With listAE, the decoder network outputs a
list of decoded message word candidates. To train the listAE, a genie is
assumed to be available at the output of the decoder. A specific loss function
is proposed to optimize the performance of a genie-aided (GA) list decoding.
The listAE is a general framework and can be used with any AE architecture. We
propose a specific architecture, referred to as incremental-redundancy AE
(IR-AE), which decodes the received word on a sequence of component codes with
non-increasing rates. Then, the listAE is trained and evaluated with both IR-AE
and Turbo-AE. Finally, we employ cyclic redundancy check (CRC) codes to replace
the genie at the decoder output and obtain a CRC aided (CA) list decoder. Our
simulation results show that the IR-AE under CA list decoding demonstrates
meaningful coding gain over Turbo-AE and polar code at low block error rates
range.Comment: 6 pages with references and 7 figure
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