A Modified Q-Learning Algorithm for Rate-Profiling of Polarization Adjusted Convolutional (PAC) Codes

• URL: http://arxiv.org/abs/2110.01563v2
• Date: Tue, 5 Oct 2021 12:15:12 GMT
• Title: A Modified Q-Learning Algorithm for Rate-Profiling of Polarization Adjusted Convolutional (PAC) Codes
• Authors: Samir Kumar Mishra, Digvijay Katyal and Sarvesha Anegundi Ganapathi
• Abstract summary: We propose a reinforcement learning based algorithm for rate-profile construction of Arikan's Polarization Assisted Convolutional (PAC) codes. We present for the first time, a set of strategies which help the reinforcement learning agent discover much better rate-profiles than those present in existing literature.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose a reinforcement learning based algorithm for rate-profile construction of Arikan's Polarization Assisted Convolutional (PAC) codes. This method can be used for any blocklength, rate, list size under successive cancellation list (SCL) decoding and convolutional precoding polynomial. To the best of our knowledge, we present, for the first time, a set of new reward and update strategies which help the reinforcement learning agent discover much better rate-profiles than those present in existing literature. Simulation results show that PAC codes constructed with the proposed algorithm perform better in terms of frame erasure rate (FER) compared to the PAC codes constructed with contemporary rate profiling designs for various list lengths. Further, by using a (64, 32) PAC code as an example, it is shown that the choice of convolutional precoding polynomial can have a significant impact on rate-profile construction of PAC codes.

Related papers

• List Decodable Quantum LDPC Codes [49.2205789216734]
  We give a construction of Quantum Low-Density Parity Check (QLDPC) codes with near-optimal rate-distance tradeoff. We get efficiently list decodable QLDPC codes with unique decoders.
  arXiv  Detail & Related papers  (2024-11-06T23:08:55Z)
• 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)
• Deep Polar Codes [19.265010348250897]
  We introduce a novel class of pre-transformed polar codes, termed as deep polar codes. We first present a deep polar encoder that harnesses a series of multi-layered polar transformations with varying sizes. Our encoding method offers flexibility in rate-profiling, embracing a wide range of code rates and blocklengths.
  arXiv  Detail & Related papers  (2023-08-06T03:29:18Z)
• Quick Dense Retrievers Consume KALE: Post Training Kullback Leibler Alignment of Embeddings for Asymmetrical dual encoders [89.29256833403169]
  We introduce Kullback Leibler Alignment of Embeddings (KALE), an efficient and accurate method for increasing the inference efficiency of dense retrieval methods. KALE extends traditional Knowledge Distillation after bi-encoder training, allowing for effective query encoder compression without full retraining or index generation. Using KALE and asymmetric training, we can generate models which exceed the performance of DistilBERT despite having 3x faster inference.
  arXiv  Detail & Related papers  (2023-03-31T15:44:13Z)
• 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)
• Scalable Polar Code Construction for Successive Cancellation List Decoding: A Graph Neural Network-Based Approach [11.146177972345138]
  This paper first maps a polar code to a heterogeneous graph called the polar-code-construction message-passing graph. Next, a graph-neural-network-based iterative message-passing algorithm is proposed which aims to find a PCCMP graph that corresponds to the polar code. Numerical experiments show that IMP-based polar-code constructions outperform classical constructions under CA-SCL decoding.
  arXiv  Detail & Related papers  (2022-07-03T19:27:43Z)
• Logistic Q-Learning [87.00813469969167]
  We propose a new reinforcement learning algorithm derived from a regularized linear-programming formulation of optimal control in MDPs. The main feature of our algorithm is a convex loss function for policy evaluation that serves as a theoretically sound alternative to the widely used squared Bellman error.
  arXiv  Detail & Related papers  (2020-10-21T17:14:31Z)
• Construction of Polar Codes with Reinforcement Learning [13.977646909897796]
  This paper formulates the polar-code construction problem for the successive-cancellation list (SCL) decoder as a maze-traversing game. The proposed method provides a novel technique for polar-code construction that no longer depends on sorting and selecting bit-channels by reliability.
  arXiv  Detail & Related papers  (2020-09-19T17:59:02Z)
• Accelerated Message Passing for Entropy-Regularized MAP Inference [89.15658822319928]
  Maximum a posteriori (MAP) inference in discrete-valued random fields is a fundamental problem in machine learning. Due to the difficulty of this problem, linear programming (LP) relaxations are commonly used to derive specialized message passing algorithms. We present randomized methods for accelerating these algorithms by leveraging techniques that underlie classical accelerated gradient.
  arXiv  Detail & Related papers  (2020-07-01T18:43:32Z)
• A PDD Decoder for Binary Linear Codes With Neural Check Polytope Projection [43.97522161614078]
  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.
  arXiv  Detail & Related papers  (2020-06-11T07:57:15Z)

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.