A PDD Decoder for Binary Linear Codes With Neural Check Polytope Projection

• URL: http://arxiv.org/abs/2006.06240v1
• Date: Thu, 11 Jun 2020 07:57:15 GMT
• Title: A PDD Decoder for Binary Linear Codes With Neural Check Polytope Projection
• Authors: Yi Wei, Ming-Min Zhao, Min-Jian Zhao and Ming Lei
• Abstract summary: We propose a PDD algorithm to address the fundamental polytope based maximum likelihood (ML) decoding problem. We also propose to integrate machine learning techniques into the most time-consuming part of the PDD decoding algorithm. We present a specially designed neural CPP (N CPP) algorithm to decrease the decoding latency.
• Score: 43.97522161614078
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Linear Programming (LP) is an important decoding technique for binary linear codes. However, the advantages of LP decoding, such as low error floor and strong theoretical guarantee, etc., come at the cost of high computational complexity and poor performance at the low signal-to-noise ratio (SNR) region. In this letter, we adopt the penalty dual decomposition (PDD) framework and propose a PDD algorithm to address the fundamental polytope based maximum likelihood (ML) decoding problem. Furthermore, we propose to integrate machine learning techniques into the most time-consuming part of the PDD decoding algorithm, i.e., check polytope projection (CPP). Inspired by the fact that a multi-layer perception (MLP) can theoretically approximate any nonlinear mapping function, we present a specially designed neural CPP (NCPP) algorithm to decrease the decoding latency. Simulation results demonstrate the effectiveness of the proposed algorithms.

Related papers

• Factor Graph Optimization of Error-Correcting Codes for Belief Propagation Decoding [62.25533750469467]
  Low-Density Parity-Check (LDPC) codes possess several advantages over other families of codes. The proposed approach is shown to outperform the decoding performance of existing popular codes by orders of magnitude.
  arXiv  Detail & Related papers  (2024-06-09T12:08:56Z)
• Reduction from sparse LPN to LPN, Dual Attack 3.0 [1.9106435311144374]
  A new algorithm called RLPN-decoding which relies on a completely different approach was introduced. It outperforms significantly the ISD's and RLPN for code rates smaller than 0.42. This algorithm can be viewed as the code-based cryptography cousin of recent dual attacks in lattice-based cryptography.
  arXiv  Detail & Related papers  (2023-12-01T17:35:29Z)
• Machine Learning-Aided Efficient Decoding of Reed-Muller Subcodes [59.55193427277134]
  Reed-Muller (RM) codes achieve the capacity of general binary-input memoryless symmetric channels. RM codes only admit limited sets of rates. Efficient decoders are available for RM codes at finite lengths.
  arXiv  Detail & Related papers  (2023-01-16T04:11:14Z)
• Adaptive Sampling for Best Policy Identification in Markov Decision Processes [79.4957965474334]
  We investigate the problem of best-policy identification in discounted Markov Decision (MDPs) when the learner has access to a generative model. The advantages of state-of-the-art algorithms are discussed and illustrated.
  arXiv  Detail & Related papers  (2020-09-28T15:22:24Z)
• Determinantal Point Processes in Randomized Numerical Linear Algebra [80.27102478796613]
  Numerical Linear Algebra (RandNLA) uses randomness to develop improved algorithms for matrix problems that arise in scientific computing, data science, machine learning, etc. Recent work has uncovered deep and fruitful connections between DPPs and RandNLA which lead to new guarantees and improved algorithms.
  arXiv  Detail & Related papers  (2020-05-07T00:39:52Z)
• ADMM-based Decoder for Binary Linear Codes Aided by Deep Learning [40.25456611849273]
  This work presents a deep neural network aided decoding algorithm for binary linear codes. Based on the concept of deep unfolding, we design a decoding network by unfolding the alternating direction method of multipliers (ADMM)-penalized decoder. Numerical results show that the resulting DL-aided decoders outperform the original ADMM-penalized decoder.
  arXiv  Detail & Related papers  (2020-02-14T03:32:14Z)
• Pruning Neural Belief Propagation Decoders [77.237958592189]
  We introduce a method to tailor an overcomplete parity-check matrix to (neural) BP decoding using machine learning. We achieve performance within 0.27 dB and 1.5 dB of the ML performance while reducing the complexity of the decoder.
  arXiv  Detail & Related papers  (2020-01-21T12:05:46Z)
• Deep Learning Based Sphere Decoding [15.810396655155975]
  A deep learning (DL)-based sphere decoding algorithm is proposed, where the radius of the decoding hypersphere is learned by a deep neural network (DNN) The performance achieved by the proposed algorithm is very close to the optimal maximum likelihood decoding (MLD) over a wide range of signal-to-noise ratios (SNRs) The computational complexity, compared to existing sphere decoding variants, is significantly reduced.
  arXiv  Detail & Related papers  (2018-07-06T03:18:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.