1,848 research outputs found
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
A Grey-Box Approach to Automated Mechanism Design
Auctions play an important role in electronic commerce, and have been used to
solve problems in distributed computing. Automated approaches to designing
effective auction mechanisms are helpful in reducing the burden of traditional
game theoretic, analytic approaches and in searching through the large space of
possible auction mechanisms. This paper presents an approach to automated
mechanism design (AMD) in the domain of double auctions. We describe a novel
parametrized space of double auctions, and then introduce an evolutionary
search method that searches this space of parameters. The approach evaluates
auction mechanisms using the framework of the TAC Market Design Game and
relates the performance of the markets in that game to their constituent parts
using reinforcement learning. Experiments show that the strongest mechanisms we
found using this approach not only win the Market Design Game against known,
strong opponents, but also exhibit desirable economic properties when they run
in isolation.Comment: 18 pages, 2 figures, 2 tables, and 1 algorithm. Extended abstract to
appear in the proceedings of AAMAS'201
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