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Performance Analysis of Hard Decision and Soft Decision Algorithms Over In Vivo Radio Channel

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Pattern Recognition and Artificial Intelligence (MedPRAI 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1543))

Abstract

BER performance of soft and hard decisions over in-vivo radio channel using ultra-wideband (UWB) frequencies (3.10–10.60 GHz) is presented in this paper. BER performance is calculated by comparing the message decoded by soft and hard decision algorithms with the transmitted message. This article compares the BER performance of soft and hard decisions over in vivo radio channels with Eb/No = (0–14 dB). We used MATLAB for this experiment. BER performance is better with a soft decision decoding algorithm in the log domain than with a hard decision algorithm, regardless of the Eb/No Levels.

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References

  1. Mezher, M., Ilyas, M., Bayat, O., Abbasi, Q.H.: Bit error rate performance of in-vivo radio channel using maximum likelihood sequence estimation. In: 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), pp. 1–4 (2020). https://doi.org/10.1109/ICECCE49384.2020.9179248

  2. Ilyas, M., et al.: Evaluation of ultra‐wideband in vivo radio channel and its effects on system performance. Trans. Emerging Telecommun. Technol. 30(1), e3530 (2019)

    Google Scholar 

  3. Demir, A.F., et al.: In vivo wireless channel modeling. arXiv preprint arXiv:1902.08199 (2019)

    Google Scholar 

  4. Obaid, S.M., Elwi, T.A., Ilyas, M.: Fractal minkowski-shaped resonator for noninvasive biomedical measurements: blood glucose test. Progress Electromagnetics Res. C 107, 143–156 (2021)

    Article  Google Scholar 

  5. Ilyas, M., Bayat, O., Abbasi, Q.H.: Experimental analysis of ultra wideband in vivo radio channel. In 2018 26th Signal Processing and Communications Applications Conference (SIU), pp. 1–4. IEEE, May 2018

    Google Scholar 

  6. Abbasi, Q.H., Member, S., Nasir, A.A., Member, K.Y.: Cooperative In-Vivo Nano-Network Communication at Terahertz Frequencies Nano-Micro Interface Relay Nodes Data-Analysis Nano-Routers, vol. 3536, no. c, pp. 1–5 (2017)

    Google Scholar 

  7. Rathore, H., Mohamed, A., Guizani, M.: Deep learning-based security schemes for implantable medical devices. In: Energy Efficiency of Medical Devices and Healthcare Applications, pp. 109–130. Academic Press (2020)

    Google Scholar 

  8. Ilyas, M., Ucan, O.N., Bayat, O., Yang, X., Abbasi, Q.H.: Mathematical modeling of ultra wideband in vivo radio channel. IEEE Access 6, 20848–20854 (2018)

    Article  Google Scholar 

  9. Demir, A.F., Z. E. Ankaralı, Q. H. Abbasi, E. Serpedin, and H. Arslan, “T 32 |||,”, June 2016

    Google Scholar 

  10. Ilyas, M., Bayat, O., Ucan, O.N., Imran, M.A., Abbasi, Q.H.: Location dependent channel characteristics for implantable devices. In: 2019 International Conference on Advances in the Emerging Computing Technologies (AECT), pp. 1–4. IEEE, February 2020

    Google Scholar 

  11. Anatomical, H., Demir, A.F., Member, S., Ankarali, Z.E., Member, S., Abbasi, Q.H.: Anatomical Region-Specific In Vivo Wireless Communication Channel Characterization, June 2017

    Google Scholar 

  12. Szivek, J.A., Roberto, R.F., Margolis, D.S.: In Vivo Strain Measurements from Hardware and Lamina during Spine Fusion, pp. 243– 250 (2005)

    Google Scholar 

  13. Alomainy, A., Hao, Y., Yuan, Y., Liu, Y.: Modelling and characterisation of radio propagation from wireless implants at different frequencies. In 2006 European Conference on Wireless Technology, pp. 119–122. IEEE, September 2006

    Google Scholar 

  14. Shubair, R.M., Elayan, H.: A survey of in vivo WBAN communications and networking: research issues and challenges. In 2015 11th International Conference on Innovations in Information Technology (IIT), pp. 11–16. IEEE. November 2015

    Google Scholar 

  15. Yang, K., Hussain, Q., Chopra, N., Munoz, M.: Nano Communication Networks Effects of non-flat interfaces in human skin tissues on the in-vivo Tera-Hertz communication channel. Nano Commun. Netw., pp. 1–9 (2015)

    Google Scholar 

  16. Hussein, E.D., Qasem, N., Jameel, M.S., Ilyas, M., Bayat, O.: Performance optimization of microstrip patch antenna using frequency selective surfaces for 60 GHz. In 2020 28th Signal Processing and Communications Applications Conference (SIU), pp. 1–4. IEEE, October 2020

    Google Scholar 

  17. Elias, J., Mehaoua, A.: Energy-aware topology design for wireless body area networks. In: 2012 IEEE International Conference on Communications (ICC) (pp. 3409–3410). IEEE, June 2012

    Google Scholar 

  18. Özdogan, Ö., Member, S., Björnson, E., Member, S.: Massive MIMO with spatially correlated rician fading channels, vol. 67, no. 5, pp. 1–17 (2019)

    Google Scholar 

  19. Khan, J.Y., Yuce, M.R., Bulger, G., Harding, B.: Wireless body area network (WBAN) design techniques and performance evaluation. J. Med. Syst. 36(3), 1441–1457 (2012)

    Google Scholar 

  20. Science, C., Mary, Q.: Characterisation of the In-vivo Terahertz Communication Channel within the Human Body Tissues for Future Nano-Communication Networks, no, September 2015

    Google Scholar 

  21. Chopra, N., Upton, J., Philpott, M., Alomainy, A.: Characterization of Volumetric Change in Collagen using THz Time Domain Spectroscopy for In-Body Nanonetworks, vol. 1, pp. 1–2

    Google Scholar 

  22. Singh, M., Wassell, I.J.: Comparison between soft and hard decision decoding using quaternary convolutional encoders and the decomposed CPM model. In: IEEE VTS 53rd Vehicular Technology Conference, Spring 2001. Proceedings (Cat. No. 01CH37202), vol. 2, pp. 1347–1351. IEEE, May 2001

    Google Scholar 

  23. Zheng, S., Zhou, X., Chen, S., Qi, P., Yang, X.: DemodNet : Learning Soft Demodulation from Hard Information Using Convolutional Neural Network, pp. 1–5

    Google Scholar 

  24. Hassan, K., Michael, K., Mrutu, S.I.: Design of s oft viterbi algorithm decoder enhanced with non t ransmittable c odewords for storage media, vol. 7, no. 1, pp. 1–11 (2017)

    Google Scholar 

  25. Alhasan, A., Audah, L., Alabbas, A.: Energy overhead evaluation of security trust models for IoT applications. J. Theor. Appl. Inf. Technol. 98, 69–77 (2020)

    Google Scholar 

  26. Khan, I., Zafar, M.H., Ashraf, M., Kim, S.: Computationally Efficient Channel Estimation in 5G, pp. 1–12 (2018)

    Google Scholar 

  27. Hussein, Y.M., Mutlag, A.H., Al-nedawe, B.M.: Comparisons of Soft Decision Decoding Algorithms Based LDPC Wireless Communication System Comparisons of Soft Decision Decoding Algorithms Based LDPC Wireless Communication System (2021)

    Google Scholar 

  28. Jose, R., Pe, A.: Analysis of hard decision and soft decision decoding algorithms of LDPC codes in AWGN. In: 2015 IEEE International Advance Computing Conference (IACC), pp. 430–435. IEEE, June 2015

    Google Scholar 

  29. Alabbas, A.R., et al.: Performance enhancement of safety message communication via designing dynamic power control mechanisms in vehicular ad hoc networks. Comput. Intell. 37(3), 1286–1308 (2021). https://doi.org/10.1111/coin.12367

  30. Alvarado, A., Member, S., Agrell, E., Member, S.: Replacing the Soft- decision FEC Limit Paradigm in the Design of Optical Communication Systems (2015)

    Google Scholar 

  31. Jeon, T., Yoon, S., Kim, K.: Performance of Iterative Soft Decision Feedback Equalizers for Single-Carrier Transmission, vol. 12, no. 3, pp. 1280–1285 (2017)

    Google Scholar 

  32. Hewavithana, T.C., Brookes, M.: Soft decisions for dqpsk demodulation for the viterbi decoding of the convolutional codes, pp. 17–20 (2003)

    Google Scholar 

  33. Phamdo, N., Alajaji, F.: Soft-decision demodulation design for COVQ over white, colored, and ISI Gaussian channels. IEEE Trans. Commun. 48(9), 1499–1506 (2000)

    Google Scholar 

  34. Theses, M., Liu, S.: Digital Commons @ Michigan Tech Soft-decision equalization techniques for frequency selective MIMO channels Soft- Decision Equalization Techniques for Frequency Selective MIMO Channels

    Google Scholar 

  35. Abbasi, Q.H., Alomainy, A., Hao, Y.: Antenna diversity techniques for enhanced networks in healthcare (2014)

    Google Scholar 

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Author information

Authors and Affiliations

  1. The University of Mashreq, Baghdad, Iraq

    Mohanad Mezher

  2. Alsalam University College, Baghdad, Iraq

    Amjed Razzaq AlAbbas

Authors
  1. Mohanad Mezher
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  2. Amjed Razzaq AlAbbas
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Corresponding author

Correspondence to Mohanad Mezher .

Editor information

Editors and Affiliations

  1. Larbi Tebessi University, Tebessa, Algeria

    Chawki Djeddi

  2. Bahria University, Islamabad, Pakistan

    Imran Siddiqi

  3. FAST National University, Islamabad, Pakistan

    Akhtar Jamil

  4. Istanbul Sabahattin Zaim University, Istanbul, Turkey

    Alaa Ali Hameed

  5. Istanbul Sabahattin Zaim University, Istanbul, Turkey

    İsmail Kucuk

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Mezher, M., AlAbbas, A.R. (2022). Performance Analysis of Hard Decision and Soft Decision Algorithms Over In Vivo Radio Channel. In: Djeddi, C., Siddiqi, I., Jamil, A., Ali Hameed, A., Kucuk, İ. (eds) Pattern Recognition and Artificial Intelligence. MedPRAI 2021. Communications in Computer and Information Science, vol 1543. Springer, Cham. https://doi.org/10.1007/978-3-031-04112-9_22

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  • DOI: https://doi.org/10.1007/978-3-031-04112-9_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-04111-2

  • Online ISBN: 978-3-031-04112-9

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