418 research outputs found
Graph-Based Decoding in the Presence of ISI
We propose an approximation of maximum-likelihood detection in ISI channels
based on linear programming or message passing. We convert the detection
problem into a binary decoding problem, which can be easily combined with LDPC
decoding. We show that, for a certain class of channels and in the absence of
coding, the proposed technique provides the exact ML solution without an
exponential complexity in the size of channel memory, while for some other
channels, this method has a non-diminishing probability of failure as SNR
increases. Some analysis is provided for the error events of the proposed
technique under linear programming.Comment: 25 pages, 8 figures, Submitted to IEEE Transactions on Information
Theor
Towards a reconfigurable hardware architecture for implementing a LDPC module suitable for software radio systems
Forward Error Correction is a key piece in modern digital communications. When a signal is transmitted over a noisy channel, multiple errors are generated. FEC techniques are directed towards the recovery of such errors. In last years, LDPC (Low Density Parity Check) codes have attracted attention of researchers because of their excellent error correction capabilities, but for actual radios high performance is not enough since they require to communicate with other multiple radios too. In general, communication between multiple radios requires the use of different standards. In this sense, Software Defined Radio (SDR) approach allows building multi standard radios based on reconfigurability abilities which means that base components including recovery errors block must provide reconfigurable options.
In this paper, some open problems in designing and implementing reconfigurable LDPC components are presented and discussed. Some features of works in the state of the art are commented and possible research lines proposed
Improved linear programming decoding of LDPC codes and bounds on the minimum and fractional distance
We examine LDPC codes decoded using linear programming (LP). Four
contributions to the LP framework are presented. First, a new method of
tightening the LP relaxation, and thus improving the LP decoder, is proposed.
Second, we present an algorithm which calculates a lower bound on the minimum
distance of a specific code. This algorithm exhibits complexity which scales
quadratically with the block length. Third, we propose a method to obtain a
tight lower bound on the fractional distance, also with quadratic complexity,
and thus less than previously-existing methods. Finally, we show how the
fundamental LP polytope for generalized LDPC codes and nonbinary LDPC codes can
be obtained.Comment: 17 pages, 8 figures, Submitted to IEEE Transactions on Information
Theor
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
- …