51 research outputs found
Design of Non-Binary Quasi-Cyclic LDPC Codes by ACE Optimization
An algorithm for constructing Tanner graphs of non-binary irregular
quasi-cyclic LDPC codes is introduced. It employs a new method for selection of
edge labels allowing control over the code's non-binary ACE spectrum and
resulting in low error-floor. The efficiency of the algorithm is demonstrated
by generating good codes of short to moderate length over small fields,
outperforming codes generated by the known methods.Comment: Accepted to 2013 IEEE Information Theory Worksho
Design of Finite-Length Irregular Protograph Codes with Low Error Floors over the Binary-Input AWGN Channel Using Cyclic Liftings
We propose a technique to design finite-length irregular low-density
parity-check (LDPC) codes over the binary-input additive white Gaussian noise
(AWGN) channel with good performance in both the waterfall and the error floor
region. The design process starts from a protograph which embodies a desirable
degree distribution. This protograph is then lifted cyclically to a certain
block length of interest. The lift is designed carefully to satisfy a certain
approximate cycle extrinsic message degree (ACE) spectrum. The target ACE
spectrum is one with extremal properties, implying a good error floor
performance for the designed code. The proposed construction results in
quasi-cyclic codes which are attractive in practice due to simple encoder and
decoder implementation. Simulation results are provided to demonstrate the
effectiveness of the proposed construction in comparison with similar existing
constructions.Comment: Submitted to IEEE Trans. Communication
Spherical and Hyperbolic Toric Topology-Based Codes On Graph Embedding for Ising MRF Models: Classical and Quantum Topology Machine Learning
The paper introduces the application of information geometry to describe the
ground states of Ising models by utilizing parity-check matrices of cyclic and
quasi-cyclic codes on toric and spherical topologies. The approach establishes
a connection between machine learning and error-correcting coding. This
proposed approach has implications for the development of new embedding methods
based on trapping sets. Statistical physics and number geometry applied for
optimize error-correcting codes, leading to these embedding and sparse
factorization methods. The paper establishes a direct connection between DNN
architecture and error-correcting coding by demonstrating how state-of-the-art
architectures (ChordMixer, Mega, Mega-chunk, CDIL, ...) from the long-range
arena can be equivalent to of block and convolutional LDPC codes (Cage-graph,
Repeat Accumulate). QC codes correspond to certain types of chemical elements,
with the carbon element being represented by the mixed automorphism
Shu-Lin-Fossorier QC-LDPC code. The connections between Belief Propagation and
the Permanent, Bethe-Permanent, Nishimori Temperature, and Bethe-Hessian Matrix
are elaborated upon in detail. The Quantum Approximate Optimization Algorithm
(QAOA) used in the Sherrington-Kirkpatrick Ising model can be seen as analogous
to the back-propagation loss function landscape in training DNNs. This
similarity creates a comparable problem with TS pseudo-codeword, resembling the
belief propagation method. Additionally, the layer depth in QAOA correlates to
the number of decoding belief propagation iterations in the Wiberg decoding
tree. Overall, this work has the potential to advance multiple fields, from
Information Theory, DNN architecture design (sparse and structured prior graph
topology), efficient hardware design for Quantum and Classical DPU/TPU (graph,
quantize and shift register architect.) to Materials Science and beyond.Comment: 71 pages, 42 Figures, 1 Table, 1 Appendix. arXiv admin note: text
overlap with arXiv:2109.08184 by other author
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