2,020 research outputs found
Adaptive Linear Programming Decoding of Polar Codes
Polar codes are high density parity check codes and hence the sparse factor
graph, instead of the parity check matrix, has been used to practically
represent an LP polytope for LP decoding. Although LP decoding on this polytope
has the ML-certificate property, it performs poorly over a BAWGN channel. In
this paper, we propose modifications to adaptive cut generation based LP
decoding techniques and apply the modified-adaptive LP decoder to short
blocklength polar codes over a BAWGN channel. The proposed decoder provides
significant FER performance gain compared to the previously proposed LP decoder
and its performance approaches that of ML decoding at high SNRs. We also
present an algorithm to obtain a smaller factor graph from the original sparse
factor graph of a polar code. This reduced factor graph preserves the small
check node degrees needed to represent the LP polytope in practice. We show
that the fundamental polytope of the reduced factor graph can be obtained from
the projection of the polytope represented by the original sparse factor graph
and the frozen bit information. Thus, the LP decoding time complexity is
decreased without changing the FER performance by using the reduced factor
graph representation.Comment: 5 pages, 8 figures, to be presented at the IEEE Symposium on
Information Theory (ISIT) 201
Improved Successive Cancellation Decoding of Polar Codes
As improved versions of successive cancellation (SC) decoding algorithm,
successive cancellation list (SCL) decoding and successive cancellation stack
(SCS) decoding are used to improve the finite-length performance of polar
codes. Unified descriptions of SC, SCL and SCS decoding algorithms are given as
path searching procedures on the code tree of polar codes. Combining the ideas
of SCL and SCS, a new decoding algorithm named successive cancellation hybrid
(SCH) is proposed, which can achieve a better trade-off between computational
complexity and space complexity. Further, to reduce the complexity, a pruning
technique is proposed to avoid unnecessary path searching operations.
Performance and complexity analysis based on simulations show that, with proper
configurations, all the three improved successive cancellation (ISC) decoding
algorithms can have a performance very close to that of maximum-likelihood (ML)
decoding with acceptable complexity. Moreover, with the help of the proposed
pruning technique, the complexities of ISC decoders can be very close to that
of SC decoder in the moderate and high signal-to-noise ratio (SNR) regime.Comment: This paper is modified and submitted to IEEE Transactions on
Communication
How to Achieve the Capacity of Asymmetric Channels
We survey coding techniques that enable reliable transmission at rates that
approach the capacity of an arbitrary discrete memoryless channel. In
particular, we take the point of view of modern coding theory and discuss how
recent advances in coding for symmetric channels help provide more efficient
solutions for the asymmetric case. We consider, in more detail, three basic
coding paradigms.
The first one is Gallager's scheme that consists of concatenating a linear
code with a non-linear mapping so that the input distribution can be
appropriately shaped. We explicitly show that both polar codes and spatially
coupled codes can be employed in this scenario. Furthermore, we derive a
scaling law between the gap to capacity, the cardinality of the input and
output alphabets, and the required size of the mapper.
The second one is an integrated scheme in which the code is used both for
source coding, in order to create codewords distributed according to the
capacity-achieving input distribution, and for channel coding, in order to
provide error protection. Such a technique has been recently introduced by
Honda and Yamamoto in the context of polar codes, and we show how to apply it
also to the design of sparse graph codes.
The third paradigm is based on an idea of B\"ocherer and Mathar, and
separates the two tasks of source coding and channel coding by a chaining
construction that binds together several codewords. We present conditions for
the source code and the channel code, and we describe how to combine any source
code with any channel code that fulfill those conditions, in order to provide
capacity-achieving schemes for asymmetric channels. In particular, we show that
polar codes, spatially coupled codes, and homophonic codes are suitable as
basic building blocks of the proposed coding strategy.Comment: 32 pages, 4 figures, presented in part at Allerton'14 and published
in IEEE Trans. Inform. Theor
From Polar to Reed-Muller Codes: a Technique to Improve the Finite-Length Performance
We explore the relationship between polar and RM codes and we describe a
coding scheme which improves upon the performance of the standard polar code at
practical block lengths. Our starting point is the experimental observation
that RM codes have a smaller error probability than polar codes under MAP
decoding. This motivates us to introduce a family of codes that "interpolates"
between RM and polar codes, call this family , where is
the original polar code, and is an RM code.
Based on numerical observations, we remark that the error probability under MAP
decoding is an increasing function of . MAP decoding has in general
exponential complexity, but empirically the performance of polar codes at
finite block lengths is boosted by moving along the family even under low-complexity decoding schemes such as, for instance,
belief propagation or successive cancellation list decoder. We demonstrate the
performance gain via numerical simulations for transmission over the erasure
channel as well as the Gaussian channel.Comment: 8 pages, 7 figures, in IEEE Transactions on Communications, 2014 and
in ISIT'1
Relaxation Bounds on the Minimum Pseudo-Weight of Linear Block Codes
Just as the Hamming weight spectrum of a linear block code sheds light on the
performance of a maximum likelihood decoder, the pseudo-weight spectrum
provides insight into the performance of a linear programming decoder. Using
properties of polyhedral cones, we find the pseudo-weight spectrum of some
short codes. We also present two general lower bounds on the minimum
pseudo-weight. The first bound is based on the column weight of the
parity-check matrix. The second bound is computed by solving an optimization
problem. In some cases, this bound is more tractable to compute than previously
known bounds and thus can be applied to longer codes.Comment: To appear in the proceedings of the 2005 IEEE International Symposium
on Information Theory, Adelaide, Australia, September 4-9, 200
- …