Molecular Communication References

Books, Monographs, Surveys and Magazines

  • Nakano, T., Eckford, A. and Haraguchi, T., Molecular Communication, Cambridge University Press, 2013.
  • Farsad, N. et al., “A comprehensive survey of recent advancements in molecular communications,” IEEE Communications Surveys & Tutorials, vol. 18, no. 3, Third Quarter, 2016.
  • Nakano, T. et al., “Molecular communication and networking: opportunities and challenges,” IEEE Transactions on NanoBioScience, vol. 11., no. 2, pp. 135-148, 2012.
  • Akyildiz, I. et al., “Nanonetworks: a new communication paradigm,” Computer Networks, vol. 52, no. 12, pp. 2260-2279, 2008.
  • Atakan, B. et al., “Body area nano networks with molecular communications in nano medicine,” IEEE Communications Magazine, vol. 50, no. 1, 2012.

Channel Models

  • Noel, A., Cheung, K.C. and Schober, R., “Improving receiver performance of diffusive molecular communication with enzymes,” IEEE Transactions on NanoBioscience, vol. 13, no. 1, pp. 31-43, Jan. 2014.
  • Deng, Y. et al., “Modeling and simulation of molecular communication systems with a reversible adsorption receiver,” IEEE Transactions on Molecular, Biological and Multi-Scale Communications, vol. 1, no. 4, pp. 347-362, Dec. 2015.
  • Farsad, N., Kim, N.-R., Eckford, A.W. and Chae, C.-B., “Channel and noise models for nonlinear molecular communication systems,” IEEE Journal on Selected Areas in Communications, vol. 32, no. 12, pp. 2392-2401, Dec. 2014.
  • Pierobon, M. and Akyildiz, F., “A physical end-to-end model for molecular communication in nanonetworks,” IEEE Journal on Selected Areas in Communications, vol. 28, no. 4, pp. 602-611, 2010.
  • Chahibi, Y. and Akyildiz, I., “Molecular communication noise and capacity analysis for particulate drug delivery systems,” IEEE Transactions on Communications, vol. 62, no. 11, pp. 3891-3903, 2014.
  • Yilmaz, H. et al., “Interference reduction via enzyme deployment for molecular communication,” Electronic Letters, vol. 52, no. 13, pp. 1097-1096, 2016.

Signal Processing and Coding for Molecular Communications

Modulation

  • Kim, N.-R. and Chae, C.-B., “Novel modulation techniques using isomers as messenger molecules for nano communication networks via diffusion,” IEEE Journal on Selected Areas in Communications, vol. 31, no. 12, pp. 847-856, 2013.

Receiver Design

  • Noel, A., Cheung, K.C. and Schober, R., “Optimal receiver design for diffusive molecular communication with flow and additive noise,” IEEE Transactions on NanoBioScience, vol. 13, no. 3, pp. 350-362, Mar. 2014.
  • Noel, A., Cheung, K.C. and Schober, R., “Improving receiver performance of diffusive molecular communication with enzymes,” IEEE Transactions on NanoBioScience, vol. 13, no. 1, pp. 31-43, 2014.
  • Chiu, H.-J. et al., “Near-optimal low complexity receiver design for diffusion-based molecular communication,” Proc. IEEE Global Communications Conference, 2013.
  • Li, B. et al., “Local convexity inspired low-complexity non coherent signal detector for nanoscale molecular communications,” IEEE Transactions on Communications, vol. 64, pp. 2079-2091, 2016.
  • Li, B. et al., “Non-linear signal detection for molecular communications,” Proc. IEE Global Communications Conference, 2017.

Channel Coding

  • Lu, Y., Higgins, M.D. and Leeson, M.S., “Comparison of channel coding schemes for molecular communications systems,” IEEE Transactions on Communications, vol. 63, no. 11, pp. 3991-4001, Sep. 2015.

Information Theoretic Limits

Replacement Channel

  • Berger, T., Rate Distortion Theory: A Mathematical Basis for Data Compression. Prentice Hall, 1971.
  • Blackwell, D., Information Theory, McGraw-Hill, 1961.

Molecular Timing Channel

  • Srinivas, K. et al., “Molecular communication in fluid media: the additive inverse Gaussian noise channel,” IEEE Transactions on Information Theory, vol. 58, no. 7, pp. 4678-4692, 2012.
  • Li, H. et al., “Capacity of the memoryless additive inverse Gaussian noise channel,” IEEE Journal on Selected Areas in Communications, vol. 32, no. 12, pp. 2315-2329, 2014.
  • Egan, M., Deng, Y., Elkashlan, M. and Duong, T.Q., “Variance-constrained capacity of the molecular timing channel with synchronization error,” Proc. IEEE Global Communications Conference, 2014.

Poisson Channel

  • Aminian, G. et al., “Capacity of diffusion-based molecular communication networks over LTI-Poisson channels,” IEEE Transactions on Molecular, Biological and Multi-Scale Communications, vol. 1, no. 2, pp. 188-201, Jun. 2015.

Concentration Channel

  • Pierobon, M. and Akyildiz, I., “Capacity of a diffusion-based molecular communication system with channel memory and molecular noise,” IEEE Transactions on Information Theory, vol. 59, no. 2, pp. 942-954, 2013.
  • Nakano, T. et al., “Channel model and capacity analysis of molecular communication with Brownian motion,” IEEE Communications Letters, vol. 16, no. 6, pp. 797-800, 2012.

Anomaly Detection via Molecular Communications

  • Mai, T., Egan, M., Duong, T.Q. and Di Renzo, M., “Event detection in molecular communication networks,” IEEE Communications Letters, vol. 21, no. 6, pp. 1249-1252, Jun. 2017.
  • Fang, Y. et al., “Convex optimization of distributed cooperative detection in multi-receiver molecular communication,” Proc. IEEE Global Communications Conference, 2016.
  • Fang, Y. et al., “Maximum likelihood detection for collaborative molecular communication,” arXiv:1704.05623, 2017.
  • Kuscu, M. and Akan, O.B., “Maximum likelihood detection for cooperative molecular communication,” To appear in IEEE Transactions on NanoBioScience.

Coexistence in Molecular Communications

  • Egan, M., Mai, T., Duong, T.Q. and Di Renzo, M., “Coexistence in molecular communications,” available: https://hal.archives-ouvertes.fr/hal-01650966
  • Loscri, V., Vegni, A.M. and Fortino, G., “On the interaction between a nano particulate system and the human body in body area networks,” Micromachines, vol. 6, pp. 1213-1235, 2015.
  • Egan, M., Duong, T.Q., Di Renzo, M., Gorce, J.-M., Nevat, I. and Loscri, V., “Cognitive molecular communication (technical abstract),” in Proc. 3rd Workshop on Molecular Communications, (2018).
  • Egan, M., Loscri, V., Duong, T.Q. and Di Renzo, M., “Strategies for coexistence in molecular communication,” accepted for publication in IEEE Transactions on NanoBioscience.

Reaction-Diffusion Networks and Dynamical Models

  • Craciun, G., “Toric differential inclusions and a proof of the global attractor conjecture,” available: https://arxiv.org/abs/1501.02860
  • Fogler, H., Elements of Chemical Reaction Engineering, Prentice Hall, 2006.
  • Nakano, T. and Suda, T., “Molecular communication using dynamic properties of oscillating and propagating patterns in concentration of information molecules,” IEEE Transactions on Communications, vol. 65, no. 8, pp. 3386-3398, 2017.
  • Feinberg, M., “Chemical reaction network structure and the stability of complex isothermal reactors-i. the deficiency zero and deficiency one theorems,” Chemical Engineering Science, vol. 42, no. 10, pp. 2229-2268, 1987.
  • Chapman, A. and Mesbahi, M., “Advection on graphs,” Proc. IEEE Conference on Decision and Control and European Control Conference (CDC-ECC), 2011.
  • van der Schaft, A. et al., “A network dynamics approach to chemical reaction networks,” International Journal of Control, vol. 89, no. 4, 2016.
  • Brodkey, R. and Hershey, H., Transport Phenomena: A Unified Approach, McGraw-Hill Book Company, 1988.
  • Fellner, K. et al., “The entropy method for reaction-diffusion systems without detailed balance: first order chemical reaction networks,” Kinetic and Related Models, vol. 10, no. 4, pp. 1055-1087, 2017.
  • Moreau, L., “Stability of continuous-time distributed consensus algorithms,” Proc. IEEE Conference on Decision and Control (CDC), 2004.
  • Craciun, G. and Feinberg, M., “Multiple equilibria in chemical reaction networks: I. the injectivity property,” SIAM J. Appl. Math., vol. 65, no. 5, pp. 1526-1546, 2005.
  • Angeli, D. et al., “A Petri net approach to the study of persistence in chemical reaction networks,” 2006.
  • Schnell, S. and Mendoza, C., “Closed form solution for time-dependent enzyme kinetics,” J. Theor. Biol., vol. 187, pp. 207-212, 1997.
  • Gopalkrishnan, M., “A scheme for molecular computation of maximum likelihood estimators for log-linear models,” arXiv:1506.03172 
  • Craciun, G., Nazarov, F. and Pantea, C., “Persistence and permanence of mass-action and power-law dynamical systems,” SIAM J. Appl. Math., vol. 73, no. 1, pp. 305-329, 2013.
  • Horn, F. and Jackson, R., “General mass action kinetics,” Arch. Rational Mech. Anal., vol. 49, pp. 81-116, 1972.

Communication via Synthetic Cells

  • Stano, P., Rampioni, G., Carrara, P., Damiano, L., Leoni, L. and Luisi, P.L., “Semi-synthetic minimal cells as a tool for biochemical ICT,” BioSystems, vol. 109, no. 1, pp. 24–34, 2012.
  • Lentini, R., Santero, S. P., Chizzolini F., Cecchi, D., Fontana, J., Marchioretto, M., Del Bianco, C., Terrell, J. L., Spencer, A. C., Martini, L., Forlin, M., Assfalg, M., Dalla Serra, M., Bentley, W. E., and Mansy, S. S., “Integrating artificial with natural cells to translate chemical messages that direct E. coli behaviour,” Nat. Commun, vol. 5, p. 4012, 2014.
  • Lentini, R., Martn, N. Y., Forlin, M., Belmonte, L., Fontana, J., Cornella, M., Martini, L., Tamburini, S., Bentley, W. E., Jousson, O., and Mansy, S. S., “Two-Way Chemical Communication between Artificial and Natural Cells,” ACS Cent. Sci., vol. 3, no. 2, pp. 117–123, 2017.
  • Rampioni, G., D’Angelo, F., Messina, M., Zennaro, A., Kuruma, Y., Tofani, D., Leoni, L., and Stano, P., “Synthetic cells produce a quorum sensing chemical signal perceived by Pseudomonas aeruginosa,” Chem. Commun., in press, 2018.

Quorum Sensing

  • Melke, P. et al., “A cell-based model for quorum sensing in heterogeneous bacteria colonies,” PLoS Computational Biology, vol. 6, no. 6, 2010.
  • Miller, M. and Bassler, B., “Quorum sensing in bacteria,” Annual Reviews in Microbiology, vol. 55, no. 1, pp. 165-199, 2001.

Microfluidics

  • Deng, Y. et al., “A microfluidic feed forward loop pulse generator for molecular communications,” Proc. IEEE Global Communications Conference (GLOBECOM), 2017.

Dimensionless Constants

  • Noel, A., Cheung, K.C. and Schober, R., “Using dimensional analysis to assess scalability and accuracy in molecular communication,” Proc. IEEE International Conference on Communications Workshops, 2013.
  • Wick, W. et al., “Modeling duct flow for molecular communication,” available: https://arxiv.org/abs/1711.01479.

Simulation Tools

  • Noel, A. et al., “Simulating with AcCoRD: Actor-based communication via reaction-diffusion,” Nano Communication Networks, vol. 11, pp. 44-75, Mar. 2017.

Experimental Testbeds

  • Farsad, N., Guo, W. and Eckford, A.W., “Tabletop molecular communication: text messages through chemical signals,” PLoS ONE, vol. 8, no. 12, Dec. 2013.