A General and Flexible Channel Decoding Approach Based on Probabilistic Programming Language

Document Type : Research Paper

Authors

Faculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran.

Abstract

Channel coding is a vital component within digital telecommunications, helping to deal with unwanted factors like noise and enabling the establishment of a more robust communication link. Among the most renowned coding schemes are linear block codes, for which a variety of decoding methods have been proposed in recent years. This paper demonstrates how a linear block coding problem can be expressed as a Probabilistic Graphical Model (PGM). We then explain how Probabilistic Programming Languages (PPLs), which are tools for solving such PGMs, can be used to decode this type of coding. Employing the Figaro programming language, as a PPL, we have simulated the decoding of several famous linear block codes and found that the results of our proposed method closely match those of existing techniques. Our approach offers several advantages, such as the flexibility to utilize diverse inference methods, the ability to choose between hard and soft decoding dynamically, and the implementation of a wide range of coding techniques. PPLs also enable the adjustment of decoding algorithm parameters and the estimation of channel conditions, ultimately enhancing the receiver's adaptability to varying channel conditions. Finally, we discuss the advantages and disadvantages of our proposed method.

Keywords

Main Subjects

References

[6]            Kumar N, Kedia D, Purohit G (2023) A review of channel coding schemes in the 5G standard. Telecommun Syst 83, pp. 423–448.
[7]            Ali MM, Hashim SJ, Chaudhary MA, Ferré G, Rokhani FZ, Ahmad Z (2023) A Reviewing Approach to Analyze the Advancements of Error Detection and Correction Codes in Channel Coding With Emphasis on LPWAN and IoT Systems. IEEE Access 11, pp. 127077–127097.
[8]            Sunny J, Ameenudeen PE, Kumar RH (2022) Design of Machine learning based Decoding Algorithms for Codes on Graph. In Proceedings - 2022 IEEE Silchar Subsection Conference, SILCON 2022 IEEE, pp. pp. pp. 1–7.
[9]            Luo FL, Zhang CJ (2017) Signal processing for 5G: Algorithms and implementations, John Wiley & Sons, Ltd, Chichester, UK.
[10]          Wang J, Tang C, Huang H, Wang H, Li J (2021) Blind identification of convolutional codes based on deep learning. Digit Signal Process A Rev J 115, pp. 103086.
[11]          Li S, Zhou J, Huang Z, Hu X (2021) Recognition of error correcting codes based on CNN with block mechanism and embedding. Digit Signal Process A Rev J 111, pp. 102986.
[12]          Guo J, Cao C, Shi D, Chen J, Zhang S, Huo X, Kong D, Li J, Tian Y, Guo M (2021) Matching Pursuit Algorithm for Decoding of Binary LDPC Codes. Wirel Commun Mob Comput 2021, pp. 1–5.
[13]          Singels R (2016) The application of Probabilistic Graphical Models to Raptor codes over Binary Input Memoryless Symmetric Channel models.
[14]          Ścibior A, Ghahramani Z, Gordon AD (2016) Practical probabilistic programming with monads, Manning Publications Co.
[15]          Obeid AK, Bruza PD, Wittek P (2019) Evaluating probabilistic programming languages for simulating quantum correlations. PLoS One 14, pp. e0208555.
[16]          Yadav A, Kakde S, Khobragade A, Bhoyar D, Kamble S (2018) LDPC Decoder ’ s Error Performance over AWGN Channel using. Int J pure Appl Math 118, pp. 3875–3879.
[17]          Ly A, Yao YD (2021) A review of deep learning in 5G research: Channel coding, massive MIMO, multiple access, resource allocation, and network security. IEEE Open J Commun Soc 2, pp. 396–408.
[18]          Carlson AB, Crilly PB, Rutledge JC (2002) Communication systems : an introduction to signals and noise in electrical communication, McGraw-Hill.
[19]          Kumar RD, Vishvaksenan KS (2020) Interference cancellation in cognitive radio-based MC-CDMA system using pre-coding technique. J Supercomput 76, pp. 1–15.
[20]          Goldsmith A (2005) Wireless communications, Cambridge University Press.
[21]          Tanenbaum AS, Wetherall DJ (2013) Computer Networks, Pearson.
[22]          Fanari L, Iradier E, Bilbao I, Cabrera R, Montalban J, Angueira P, Seijo O, Val I (2022) A Survey on FEC Techniques for Industrial Wireless Communications. IEEE Open J Ind Electron Soc 3, pp. 674–699.
[23]          Tomlinson M, Tjhai CJ, Ambroze MA, Ahmed M, Jibril M (2017) Reed–Solomon Codes and Binary Transmission. In, Tomlinson M, Tjhai CJ, Ambroze MA, Ahmed M, Jibril M, eds. Springer International Publishing, Cham, pp. pp. pp. 167–179.
[24]          Fanari L, Iradier E, Bilbao I, Cabrera R, Montalban J, Angueira P (2021) Comparison between different channel coding techniques for ieee 802.11be within factory automation scenarios. Sensors 21, pp. 7209.
[25]          Krapu C, Borsuk M (2019) Probabilistic programming: A review for environmental modellers. Environ Model Softw 114, pp. 40–48.
[26]          Le Bras R, Arora N, Kushida N, Mialle P, Bondár I, Tomuta E, Alamneh FK, Feitio P, Villarroel M, Vera B, Sudakov A, Laban S, Nippress S, Bowers D, Russell S, Taylor T (2021) NET-VISA from Cradle to Adulthood. A Machine-Learning Tool for Seismo-Acoustic Automatic Association. Pure Appl Geophys 178, pp. 2437–2458.
[27]          Lake BM, Salakhutdinov R, Tenenbaum JB (2015) Human-level concept learning through probabilistic program induction.
[28]          Ruttenberg B, Kellogg L, Pfeffer A (2016) Probabilistic Programming for Malware Analysis. CoRR abs/1603.0,.
[29]          Farnoudkia H, Purutçuoglu V (2019) Copula Gaussian graphical modeling of biological networks and Bayesian inference of model parameters. Sci Iran 26, pp. 2495–2505.
[30]          Alabady SA, Mohd Salleh MF, Al-Turjman F (2018) LCPC error correction code for IoT applications. Sustain Cities Soc 42, pp. 663–673.
[31]          Koike-Akino T, Wang Y (2021) Protograph-Based Design for QC Polar Codes. In IEEE International Symposium on Information Theory - Proceedings IEEE, pp. pp. pp. 593–598.
[32]          Sadlier RJ, Humble TS (2016) Superdense Coding Interleaved with Forward Error Correction. Quantum Meas Quantum Metrol 3,.
[33]          Sarnin SS, Kadri N, Mozi AM, Wahab NA, Naim NF (2010) Performance analysis of BPSK and QPSK using error correcting Code through AWGN. ICNIT 2010 - 2010 Int Conf Netw Inf Technol pp. 178–182.
[34]          Giordano AA, Levesque AH (2015) BER performance of BPSK in AWGN with a Hamming code. In Modeling of digital communications systems using Simulink Wiley, pp. pp. pp. 175–180.
[35]          Karimi-Lenji A, Houshmand M, Zarmehi F (2017) A high-performance belief propagation decoding algorithm for codes with short cycles. Int J Commun Syst 30, pp. 1–8.
[36]          Old J, Rispler M (2023) Generalized Belief Propagation Algorithms for Decoding of Surface Codes. Quantum 7, pp. 1037.
[37]          Habib S, Mitchell DGM (2023) Reinforcement Learning for Sequential Decoding of Generalized LDPC Codes. In 2023 12th International Symposium on Topics in Coding (ISTC) IEEE, pp. pp. pp. 1–5.
[38]          Fang M (2023) An Improved Min-Sum Polar Code Decoding Algorithm. In 2023 3rd Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS) IEEE, pp. pp. pp. 655–658.
[39]          Huang L, Zhang H, Li R, Ge Y, Wang J (2020) AI Coding: Learning to Construct Error Correction Codes. IEEE Trans Commun 68, pp. 26–39.
[40]          Qin Z, Fei Z, Huang J, Wang Y, Xiao M, Yuan J (2023) Reinforcement-Learning-Based Overhead Reduction for Online Fountain Codes With Limited Feedback. IEEE Trans Commun 71, pp. 3977–3991.
[41]          Song D, Ren J, Wang L, Chen G (2022) Designing a Common DP-LDPC Code Pair for Variable On-Body Channels. IEEE Trans Wirel Commun 21, pp. 9596–9609.
[42]          Jeevangi S, Jawaligi S, Patil V (2022) Deep Learning-based SNR Estimation for Multistage Spectrum Sensing in Cognitive Radio Networks. J Telecommun Inf Technol.
[43]          Venkateswarlu C, Rao NV (2022) Optimal channel estimation and interference cancellation in MIMO-OFDM system using MN-based improved AMO model. J Supercomput 78, pp. 3402–3424.
[44]          Khazali A, Bozorgchenani A, Tarchi D, Shayesteh MG, Kalbkhani H (2023) Joint Task Assignment, Power Allocation and Node Grouping for Cooperative Computing in NOMA-mmWave Mobile Edge Computing. IEEE Access 11, pp. 93664–93678.
[45]          Naik C (2022) Computational Intelligence Algorithms For Energy Optimization Problems In Wireless Sensor Networks.

Files

History

  • Receive Date: 27 February 2024
  • Revise Date: 28 May 2024
  • Accept Date: 06 January 2025

Share

How to cite

Statistics