1,297 research outputs found
Low Complexity Belief Propagation Polar Code Decoders
Since its invention, polar code has received a lot of attention because of
its capacity-achieving performance and low encoding and decoding complexity.
Successive cancellation decoding (SCD) and belief propagation decoding (BPD)
are two of the most popular approaches for decoding polar codes. SCD is able to
achieve good error-correcting performance and is less computationally expensive
as compared to BPD. However SCDs suffer from long latency and low throughput
due to the serial nature of the successive cancellation algorithm. BPD is
parallel in nature and hence is more attractive for high throughput
applications. However since it is iterative in nature, the required latency and
energy dissipation increases linearly with the number of iterations. In this
work, we borrow the idea of SCD and propose a novel scheme based on
sub-factor-graph freezing to reduce the average number of computations as well
as the average number of iterations required by BPD, which directly translates
into lower latency and energy dissipation. Simulation results show that the
proposed scheme has no performance degradation and achieves significant
reduction in computation complexity over the existing methods.Comment: 6 page
An Iteratively Decodable Tensor Product Code with Application to Data Storage
The error pattern correcting code (EPCC) can be constructed to provide a
syndrome decoding table targeting the dominant error events of an inter-symbol
interference channel at the output of the Viterbi detector. For the size of the
syndrome table to be manageable and the list of possible error events to be
reasonable in size, the codeword length of EPCC needs to be short enough.
However, the rate of such a short length code will be too low for hard drive
applications. To accommodate the required large redundancy, it is possible to
record only a highly compressed function of the parity bits of EPCC's tensor
product with a symbol correcting code. In this paper, we show that the proposed
tensor error-pattern correcting code (T-EPCC) is linear time encodable and also
devise a low-complexity soft iterative decoding algorithm for EPCC's tensor
product with q-ary LDPC (T-EPCC-qLDPC). Simulation results show that
T-EPCC-qLDPC achieves almost similar performance to single-level qLDPC with a
1/2 KB sector at 50% reduction in decoding complexity. Moreover, 1 KB
T-EPCC-qLDPC surpasses the performance of 1/2 KB single-level qLDPC at the same
decoder complexity.Comment: Hakim Alhussien, Jaekyun Moon, "An Iteratively Decodable Tensor
Product Code with Application to Data Storage
Coordinated design of coding and modulation systems
The joint optimization of the coding and modulation systems employed in telemetry systems was investigated. Emphasis was placed on formulating inner and outer coding standards used by the Goddard Spaceflight Center. Convolutional codes were found that are nearly optimum for use with Viterbi decoding in the inner coding of concatenated coding systems. A convolutional code, the unit-memory code, was discovered and is ideal for inner system usage because of its byte-oriented structure. Simulations of sequential decoding on the deep-space channel were carried out to compare directly various convolutional codes that are proposed for use in deep-space systems
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