Skip to main content

Advertisement

SpringerLink
Log in

Performance Analysis of Convolutional Coding Techniques in Diffusion-Based Concentration-Encoded PAM Molecular Communication Systems

  • Published:
BioNanoScience Aims and scope Submit manuscript

Abstract

In this paper, we investigate the performance of convolutional coding techniques in an ideal diffusion-based concentration-encoded molecular communication (CEMC) channel with pulse amplitude modulation (PAM) transmission scheme. Convolutional codes have shown to provide a significant amount of gain in the signal-to-interference strength (energy) ratio between a pair of communicating nanomachines and thus help increase the communication range and improve the end-to-end system performance. We have also determined the impact of convolutional codes for several key performance factors, e.g. transmission data rate, communication range, and the constraint length of the convolutional code in the context of diffusion-based CEMC. In addition, we have compared the PAM system with the impulse modulation system in CEMC. We have found that the use of a multilevel (M-ary) signalling scheme in the CEMC system provides a degraded bit error rate performance even in short communication ranges. Therefore, a convolutional coded scheme with larger alphabet size should not be recommended for a CEMC system because of the stronger intersymbol interference produced within symbols. As an alternative, a convolutional coded scheme with binary alphabet and higher symbol rate was evaluated and found to provide a gain in the signal-to-interference energy ratio of the system and is therefore recommended for short- to medium-range CEMC. For long communication ranges, an uncoded system can rather be preferred to convolutional coded schemes.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Notes

  1. Nanoscale refers to the dimension in the range from 1 to 100 nm. One nanometre is equal to 10−9 m.

  2. The term message refers to a block of k bits that is formed and treated as an “entity” by all units of the system.

  3. We assume that the used communication system is one dimensional. BPSK, ASK [13] are one dimensional. QPSK, QAM are two dimensional, M-FSK [13] is M dimensional. In the case of an M-dimensional communication system, z j can be expressed as \( {z_j}=\sum\nolimits_{i=1}^M {\alpha_j^i\overrightarrow{{{u_i}}}} \), where \( \alpha_j^i \) take values from the set of real numbers, and \( \overrightarrow{{{u_i}}} \) are orthogonal vectors of unit size, forming the M-dimensional space.

  4. Similar to the quantization used by PCM for the digitization of voice signals.

  5. The analogue signal at the output of the demodulator-detector is hard limited using a certain threshold; the location of the analogue output's value in reference to that threshold determines if a logic “1” or logic “0” will be pushed out of the output.

  6. Passage time is the time that a molecule needs to reach the RN after it starts from the TN. Alternatively, it can also be termed as propagation delay [33].

  7. In the OOK type of PAM, the TN transmits a pulse of molecules with amplitude Q avg and duration T b when it wants to transmit a “1” bit (i.e. the TN is on), and it does not transmit any molecule at all when it wants to transmit a “0” bit (i.e. the TN remains off). See [12, 13] for details on OOK-based PAM scheme.

  8. For binary scheme, a “symbol” is represented by a “1” or “0” bit, whereas a symbol comprises of log2M bits in M-ary scheme. M denotes the alphabet size of the system.

  9. By cross-over SIR we mean that at approximately 3 dB, the performance curves of the uncoded system and the CE system with M = 2 cross over. Correspondingly, for SIR > ∼3 dB, the performance of the 2-PAM CE system with M = 2 levels is better than that of the uncoded system, and for SIR < ∼3 dB, the performance of the 2-PAM CE system with M = 2 is worse than that of the uncoded system. The cross-over point at SIR = 3 dB indicates the condition when \( {E_S}=2{E_I} \) as shown in Eq. (14) and occurs when the signal strength is twice as much as the interference strength. See [12] for relevant details.

  10. For a single pulse transmission, the SIR is the ratio of the percentage of total number of molecules “received” (w) during a symbol (pulse) duration to that “not received” (1 − w) during the symbol duration [20] and following Eq. (16) can be expressed as SIR(r, f) = 10log10{w/(1 − w)}.

Abbreviations

ASK:

Amplitude-shift keying

BE:

Block encoded

BM:

Branch metric

BER:

Bit error rate

BPSK:

Binary phase-shift keying

BSC:

Binary symmetric channel

CC:

Convolutional codes

CE:

Convolutional encoder

CEMC:

Concentration-encoded molecular communication

CIR:

Channel impulse response

IM:

Impulse modulation

ISI:

Intersymbol interference

MC:

Molecular communication

M-FSK:

M-ary frequency-shift keying

ML:

Maximum likelihood

PAM:

Pulse amplitude modulation

PCM:

Pulse code modulation

PM:

Path metric

QAM:

Quadrature amplitude modulation

QPSK:

Quadrature phase-shift keying

RIS:

Received information sequence

RN:

Receiving nanomachine

TIS:

Transmitted information sequence

TN:

Transmitting nanomachine

References

  1. Akyildiz, I. F., Brunetti, F., & Blazquez, C. (2008). Nanonetworks: a new communication paradigm. Computer Networks Journal (Elsevier), 52, 2260–2279.

    Article  Google Scholar 

  2. Moritani, Y., Hiyama, S., Suda, T. (2006). Molecular communication for health care applications. In Pervasive computing and communications workshops, 2006. PerCom Workshops 2006. Fourth Annual IEEE International Conference on, 2006, 5 pp. 553.

  3. Moritani, Y., Hiyama, S., Suda, T. (2007). Molecular communication. A biochemically-engineered communication system. In Frontiers in the convergence of bioscience and information technologies, 2007. FBIT 2007, 2007, pp. 839–844

  4. Nakano, T., Moore, M. J., Wei, F., Vasilakos, A. V., & Shuai, J. (2012). Molecular communication and networking: opportunities and challenges. NanoBioscience, IEEE Transactions on, 11, 135–148.

    Article  Google Scholar 

  5. IEEE, Emerging Technical Subcommittee on Nanoscale, Molecular, and Quantum Networking. (2012). Web. http://nanonetworks.org/. Accessed on 04 May 2013.

  6. IEEE, P1906.1-recommended practice for nanoscale and molecular communication framework, active project. Web. http://standards.ieee.org/develop/project/1906.1.html. Accessed on 04 May 2013.

  7. Mahfuz, M. U., Makrakis, D., & Mouftah, H. T. (2010). On the characterization of binary concentration-encoded molecular communication in nanonetworks. Nano Communication Networks Journal (Elsevier), 1, 289–300.

    Article  Google Scholar 

  8. Mahfuz, M.U., Makrakis, D., Mouftah, H.T. (2010). Characterization of molecular communication channel for nanoscale networks. In Proc. 3rd International Conference on Bio-Inspired Systems and Signal Processing (BIOSIGNALS-2010), Valencia, Spain, 2010, pp. 327–332.

  9. Nakano, T., Moore, M., Enomoto, A., & Suda, T. (2011). Molecular communication technology as a biological ICT. In H. Sawai (Ed.), Biological functions for information and communication technologies (pp. 49–86). Berlin: Springer.

    Chapter  Google Scholar 

  10. Moore, M. J., Enomoto, A., Suda, T., Nakano, T., & Okaie, Y. (2007). Molecular communication: new paradigm for communication among nano-scale biological machines In H. Bidgoli (Ed.), The handbook of computer networks. Hoboken: Wiley.

    Google Scholar 

  11. Pierobon, M., & Akyildiz, I. F. (2010). A physical end-to-end model for molecular communication in nanonetworks. Selected Areas in Communications, IEEE Journal on, 28, 602–611.

    Article  Google Scholar 

  12. Mahfuz, M.U., Makrakis, D., Mouftah, H.T. (2011). A comprehensive study of concentration-encoded unicast molecular communication with binary pulse transmission, in Nanotechnology (IEEE-NANO), 2011 11th IEEE Conference on, 2011, pp. 227–232.

  13. Haykin, S. (2000). Communication systems. Chichester: Wiley.

    Google Scholar 

  14. Park, H. (1997). Coded modulation and equalization for wireless infrared communications. Ph.D. Thesis, School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta.

  15. Alexander, S. B. (1997). Optical communication receiver design. Bellingham: SPIE.

    Book  Google Scholar 

  16. Roberts, O.F.T. (1923). The theoretical scattering of smoke in a turbulent atmosphere. Proceedings of the Royal Society of London.Series A, Containing Papers of a Mathematical and Physical Character, vol. 104, pp. 640–654, Dec. 1, 1923.

  17. Bossert, W. H., & Wilson, E. O. (1963). The analysis of olfactory communication among animals. Journal of Theoretical Biology, 5, 443–469.

    Article  Google Scholar 

  18. Mahfuz, M.U., Makrakis, D., Mouftah, H.T. (2011). On the characteristics of concentration-encoded multi-level amplitude modulated unicast molecular communication. In Electrical and Computer Engineering (CCECE), 2011 24th Canadian Conference on, 2011, pp. 000312–000316.

  19. Mahfuz, M.U., Makrakis, D., Mouftah, H.T. (2011). On the detection of binary concentration-encoded unicast molecular communication in nanonetworks. In Proc. 4th International Conference on Bio-Inspired Systems and Signal Processing (BIOSIGNALS-2011), 26–29 January, 2011. Rome, Italy., 2011, pp. 446–449.

  20. Mahfuz, M.U., Makrakis, D., Mouftah, H.T. (2012). Strength based receiver architecture and communication range and rate dependent signal detection characteristics of concentration encoded molecular communication. In Proc. BWCCA-2012, Victoria, Canada, 2012, pp. 28–35.

  21. Mahfuz, M.U., Makrakis, D., Mouftah, H.T. (2013). Sampling based optimum signal detection in concentration-encoded molecular communication receiver architecture and performance. In Proc. 6th International Conference on Bio-Inspired Systems and Signal Processing (BIOSIGNALS-2013), Barcelona, Spain, 2013.

  22. Leeson, M.S., Higgins, M.D. (2012). Error correction coding for molecular communications. In IEEE MoNaCom 2012 Workshop, ICC-2012, Ottawa, Canada, 2012.

  23. Lin, S. (2004). Error control coding: fundamentals and applications. Upper Saddle River: Pearson-Prentice Hall.

    Google Scholar 

  24. Jiang, Y. (2010). A practical guide to error control coding using Matlab. Norwood, MA: Artech House.

  25. Lee, E. A., & Messerschmitt, D. G. (1994). Digital Communication. USA: Kluwer Academic Publishers.

    Book  Google Scholar 

  26. Lin, S. (1983). Error control coding: fundamentals and applications. Englewood Cliffs: Prentice-Hall.

    Google Scholar 

  27. Wozencraft, J. M. (1957). Sequential decoding for reliable communications. IRE Nat Conv Rec, 5, 11–25.

    Google Scholar 

  28. Massey, J. L. (1963). Threshold decoding. Cambridge: The MIT Press.

    Google Scholar 

  29. Viterbi, A. (1967). Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. Information Theory, IEEE Transactions, 13, 260–269.

    Article  MATH  Google Scholar 

  30. Berg, H. C. (1993). Random walks in biology. NJ: Princeton University Press.

    Google Scholar 

  31. Moore, M., Suda, T., & Oiwa, K. (2009). Molecular communication: modeling noise effects on information rate. NanoBioscience, IEEE Transactions on, 8, 169–180.

    Article  Google Scholar 

  32. Atakan, B., & Akan, O. B. (2010). Deterministic capacity of information flow in molecular nanonetworks. Nano Communication Networks, 1, 31–42.

    Article  Google Scholar 

  33. Eckford, A.W. (2007). Nanoscale communication with brownian motion. In Information Sciences and Systems, 2007. CISS '07. 41st Annual Conference on, 2007, pp. 160–165.

  34. Moore, M. J., & Nakano, T. (2011). Synchronization of inhibitory molecular spike oscillators. BIONETICS, 103, 183–195.

    Google Scholar 

  35. Carslaw, H. S., & Jaeger, J. C. (1959). Conduction of heat in solids. Great Britain: Oxford at the Clarendon Press.

    Google Scholar 

  36. Crank, J. (1975). The mathematics of diffusion. Oxford: Clarendon Press.

    Google Scholar 

  37. Mahfuz, M.U., Makrakis, D., Mouftah, H.T. (2011). Transient characterization of concentration-encoded molecular communication with sinusoidal stimulation. In Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies (ISABEL '11), Article 14 , 6 Pages. Barcelona, Spain, 2011.

  38. Lacasa, N.R. (2009). Modeling the molecular communication nanonetworks. M.Sc. thesis, The Universitat Politècnica de Catalunya (UPC), Spain.

  39. Mano, M. (2002). Digital design. Upper Saddle River: Prentice-Hall.

    Google Scholar 

Download references

Acknowledgments

M.U. Mahfuz would like to thank the Natural Sciences and Engineering Research Council of Canada (NSERC) for the financial support in the form of PGS-D scholarship during the years 2010 to 2013.

Author information

Authors and Affiliations

  1. School of Electrical Engineering and Computer Science, University of Ottawa, 800 King Edward Ave, Ottawa, ON, Canada, K1N 6N5

    Mohammad Upal Mahfuz, Dimitrios Makrakis & Hussein T. Mouftah

Authors
  1. Mohammad Upal Mahfuz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dimitrios Makrakis
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hussein T. Mouftah
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad Upal Mahfuz.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Mahfuz, M.U., Makrakis, D. & Mouftah, H.T. Performance Analysis of Convolutional Coding Techniques in Diffusion-Based Concentration-Encoded PAM Molecular Communication Systems. BioNanoSci. 3, 270–284 (2013). https://doi.org/10.1007/s12668-013-0086-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12668-013-0086-5

Keywords

Search

Navigation