1,751 research outputs found
Scalable Successive-Cancellation Hardware Decoder for Polar Codes
Polar codes, discovered by Ar{\i}kan, are the first error-correcting codes
with an explicit construction to provably achieve channel capacity,
asymptotically. However, their error-correction performance at finite lengths
tends to be lower than existing capacity-approaching schemes. Using the
successive-cancellation algorithm, polar decoders can be designed for very long
codes, with low hardware complexity, leveraging the regular structure of such
codes. We present an architecture and an implementation of a scalable hardware
decoder based on this algorithm. This design is shown to scale to code lengths
of up to N = 2^20 on an Altera Stratix IV FPGA, limited almost exclusively by
the amount of available SRAM
The Way Forward: From Sanctions to Supports
The New York City Working Group on School Transformation brought together education practitioners, school reformers, policy-makers, advocates, and parent and student leaders to propose alternatives to the school closings policy of the New York City Department of Education (DOE). (See the list of Working Group members in Appendix 1.) The group was initiated by the New York City Coalition for Educational Justice and coordinated by the Annenberg Institute for School Reform following the fall 2011 conference Effective Alternatives to School Closings: Transforming Struggling Schools in New York City. This report presents the Working Group's conclusions about the limitations of school closings and a set of recommendations for systemic responses to the needs of struggling schools
Improved Successive Cancellation Flip Decoding of Polar Codes Based on Error Distribution
Polar codes are a class of linear block codes that provably achieves channel
capacity, and have been selected as a coding scheme for generation
wireless communication standards. Successive-cancellation (SC) decoding of
polar codes has mediocre error-correction performance on short to moderate
codeword lengths: the SC-Flip decoding algorithm is one of the solutions that
have been proposed to overcome this issue. On the other hand, SC-Flip has a
higher implementation complexity compared to SC due to the required
log-likelihood ratio (LLR) selection and sorting process. Moreover, it requires
a high number of iterations to reach good error-correction performance. In this
work, we propose two techniques to improve the SC-Flip decoding algorithm for
low-rate codes, based on the observation of channel-induced error
distributions. The first one is a fixed index selection (FIS) scheme to avoid
the substantial implementation cost of LLR selection and sorting with no cost
on error-correction performance. The second is an enhanced index selection
(EIS) criterion to improve the error-correction performance of SC-Flip
decoding. A reduction of in the implementation cost of logic elements
is estimated with the FIS approach, while simulation results show that EIS
leads to an improvement on error-correction performance improvement up to
dB at a target FER of .Comment: This version of the manuscript corrects an error in the previous
ArXiv version, as well as the published version in IEEE Xplore under the same
title, which has the DOI:10.1109/WCNCW.2018.8368991. The corrections include
all the simulations of SC-Flip-based and SC-Oracle decoders, along with
associated comments in-tex
Selective Decoding in Associative Memories Based on Sparse-Clustered Networks
Associative memories are structures that can retrieve previously stored
information given a partial input pattern instead of an explicit address as in
indexed memories. A few hardware approaches have recently been introduced for a
new family of associative memories based on Sparse-Clustered Networks (SCN)
that show attractive features. These architectures are suitable for
implementations with low retrieval latency, but are limited to small networks
that store a few hundred data entries. In this paper, a new hardware
architecture of SCNs is proposed that features a new data-storage technique as
well as a method we refer to as Selective Decoding (SD-SCN). The SD-SCN has
been implemented using a similar FPGA used in the previous efforts and achieves
two orders of magnitude higher capacity, with no error-performance penalty but
with the cost of few extra clock cycles per data access.Comment: 4 pages, Accepted in IEEE Global SIP 2013 conferenc
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