Joint source and channel coding with systematic polar codes for wireless sensor communication in next generation networks

Original Research

Abstract

Polar codes have strongly entered into action within the standardization of the next generation 5G mobile communication systems, which are expected to be an enabling technology for the Internet of Things where networks with a large number of sensors have to handle massive connectivity demands. This paper proposes and investigates the use of systematic polar codes for joint source-channel coding of correlated sources in wireless sensor networks, thus allowing the compression of the volume of data to be transmitted over the network on one hand, and on the other hand, the protection of this data from channel impairments. Results show that systematic polar codes can achieve a distributed compression with rates close to theoretical limits, with better error rates obtained for larger blocks, and a better robustness against transmission errors obtained with stronger compression and shorter block lengths. Furthermore, while the system is able to overcome the effect of noise on parity information with adequate power management, noisy side information significantly degrades system performance with remarkable gaps towards the case of distributed compression with an ideal transmission channel.

Keywords

Channel coding Compression Distributed source coding Entropy Systematic polar codes Wireless sensor networks 

Notes

References

  1. Aaron A, Zhang R, Girod B (2002) Wyner-ziv coding of motion video. In: Signals, systems and computers. Conference record of the thirty-sixth Asilomar conference, IEEE, vol 1, pp 240–244Google Scholar
  2. Arikan E (2009) Channel polarization: a method for constructing capacity-achieving codes for symmetric binary-input memoryless channels. IEEE Trans Inf Theory 55(7):3051–3073MathSciNetCrossRefMATHGoogle Scholar
  3. Arikan E (2011) Systematic polar coding. IEEE Commun Lett 15(8):860–862CrossRefGoogle Scholar
  4. Ascenso J, Pereira F (2009) Complexity efficient stopping criterion for LDPC based distributed video coding. In: Proceedings of the 5th international ICST mobile multimedia communications conference, London, UKGoogle Scholar
  5. Berrou C, Glavieux A, Thitimajshima P (1993) Near Shannon limit error-correcting coding and decoding: turbo-codes. In: Communications, 1993. ICC’93 Geneva. Technical program, conference record, IEEE international conference, IEEE, vol 2, pp 1064–1070Google Scholar
  6. Douillard C, Jézéquel M, Berrou C, Brengarth N, Tousch J, Pham N (2000) The turbo code standard for DVB-RCS. In: 2nd international symposium on turbo codes and related topics, Brest, pp 535–538Google Scholar
  7. Farah J, Yaacoub C, Rachkidy N, Marx F (2006) Binary and non-binary turbo codes for the compression of correlated sources transmitted through error-prone channels. In: Turbo codes and related topics; 6th international ITG-conference on source and channel coding (TURBOCODING), 2006 4th international symposium, VDE, Munich, Germany, pp 1–6Google Scholar
  8. Gallager RG (1968) Information theory and reliable communication, vol 2. Springer, BerlinMATHGoogle Scholar
  9. Haykin S (2001) Communication systems. Chap 4. Wiley, Hoboken, pp 247–308Google Scholar
  10. HUAWEI (2015) White paper. 5g: new air interface and radio access virtualization. Tech. rep., HUAWEI Technologies CoGoogle Scholar
  11. Huffman DA (1952) A method for the construction of minimum-redundancy codes. Proc IRE 40(9):1098–1101CrossRefMATHGoogle Scholar
  12. Iscan O, Lentner D, Xu W (2016) A comparison of channel coding schemes for 5g short message transmission. In: Globecom Workshops (GC Wkshps), 2016 IEEE, Washington, DC, USA, pp 1–6Google Scholar
  13. Korada SB, Urbanke RL (2010) Polar codes are optimal for lossy source coding. IEEE Trans Inf Theory 56(4):1751–1768MathSciNetCrossRefMATHGoogle Scholar
  14. Liveris AD, Xiong Z, Georghiades CN (2002) Compression of binary sources with side information at the decoder using LDPC codes. IEEE Commun Lett 6(10):440–442CrossRefGoogle Scholar
  15. Lv X, Liu R, Wang R (2013) A novel rate-adaptive distributed source coding scheme using polar codes. IEEE Commun Lett 17(1):143–146CrossRefGoogle Scholar
  16. Molano JIR, Lovelle JMC, Montenegro CE, Granados JJR, Crespo RG (2017) Metamodel for integration of internet of things, social networks, the cloud and industry 4.0. J Ambient Intell Humaniz Comput.  https://doi.org/10.1007/s12652-017-0469-5
  17. Onay S (2014) Polar codes for distributed source coding. PhD thesis, Bilkent UniversityGoogle Scholar
  18. Ryoo I, Sun K, Lee J, Kim S (2017) A 3-dimensional group management MAC scheme for mobile IOT devices in wireless sensor networks. J Ambient Intell Humaniz Comput.  https://doi.org/10.1007/s12652-017-0557-6
  19. Sartipi M, Fekri F (2005) Distributed source coding in wireless sensor networks using LDPC coding: the entire Slepian-wolf rate region. In: Wireless communications and networking conference, 2005 IEEE, vol 4, New Orleans, LA, USA, pp 1939–1944Google Scholar
  20. Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27:379–423, 623–656Google Scholar
  21. Slepian D, Wolf J (1973) Noiseless coding of correlated information sources. IEEE Trans Inf Theory 19(4):471–480MathSciNetCrossRefMATHGoogle Scholar
  22. Tong W, Ma J, Huawei PZ (2015) Enabling technologies for 5g air-interface with emphasis on spectral efficiency in the presence of very large number of links. In: Communications (APCC), 2015 21st Asia–Pacific conference, IEEE, Kyoto, Japan, pp 184–187Google Scholar
  23. Trang VTT, Kang JW, Jang M, Kim Jh, Kimy SH (2012) The performance of polar codes in distributed source coding. In: Communications and electronics (ICCE), 2012 fourth international conference, IEEE, Hue, Vietnam, pp 196–199Google Scholar
  24. Vangala H, Viterbo E, Hong Y (2015) Polar coding algorithms in MATLAB. http://www.ecse.monash.edu.au/staff/eviterbo/polarcodes.html. Accessed 15 Feb 2018
  25. Vangala H, Hong Y, Viterbo E (2016) Efficient algorithms for systematic polar encoding. IEEE Commun Lett 20(1):17–20CrossRefGoogle Scholar
  26. Yaacoub C, Sarkis M (2016) Distributed compression of correlated sources using systematic polar codes. In: 2016 9th international symposium on turbo codes and iterative information processing (ISTC), Brest, France, pp 96–100.  https://doi.org/10.1109/ISTC.2016.7593084
  27. Yaacoub C, Sarkis M (2017) Systematic polar codes for joint source-channel coding in wireless sensor networks and the internet of things. Proced Comput Sci 110:266–273.  https://doi.org/10.1016/j.procs.2017.06.094 CrossRefGoogle Scholar
  28. Yaacoub C, Farah J, Pesquet-Popescu B (2007) Joint source-channel Wyner-Ziv coding in wireless video sensor networks. In: Signal processing and information technology, 2007 IEEE international symposium, IEEE, pp 225–228Google Scholar
  29. Yaacoub C, Farah J, Pesquet-Popescu B (2008) Feedback channel suppression in distributed video coding with adaptive rate allocation and quantization for multiuser applications. EURASIP J Wirel Commun Netw.  https://doi.org/10.1155/2008/427247
  30. Yaacoub C, Farah J, Pesquet-Popescu B (2009) New adaptive algorithms for GOP size control with return channel suppression in Wyner-Ziv video coding. Int J Digit Multimed Broadcast.  https://doi.org/10.1155/2009/319021
  31. Zhang B, Shen H, Yin B, Lu L, Chen D, Wang T, Gu L, Wang X, Hou X, Jiang H, Benjebbour A, Kishiyama Y (2016) A 5g trial of polar code. In: 2016 IEEE Globecom Workshops (GC Wkshps), Washington, DC, USA, pp 1–6.  https://doi.org/10.1109/GLOCOMW.2016.7848800

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of EngineeringHoly Spirit University of Kaslik (USEK)JouniehLebanon

Personalised recommendations