Effect of ISI Mitigation on Modulation Techniques in Communication via Diffusion

Communication via diffusion (CvD) is an effective and energy efficient method for transmitting information in nanonetworks. In this work, we focus on a diffusion-based communication system where the reception process is an absorption via receptors. Whenever a molecule hits to the receiver it is removed from the environment. This kind of reception process is called first passage process and it is more complicated compared to diffusion process only. In 3-D environments, obtaining analytical solution for hitting time distribution for realistic cases is complicated, hence we develop an end-to-end simulator for he diffusion-based communication system that sends consecutive symbols. In CvD, each symbol is modulated and demodulated in a time slot called symbol duration, however the long tail distribution of hitting time is the main challenge that affects the symbol detection error. The molecules arriving in the following slots become an interference source when detection takes place. End-to-end simulator enables us to analyze the effect of inter symbol interference (ISI) without making any assumptions on the ISI. We propose an ISI cancellation technique that utilizes decision feedback for compensating the effect of previously demodulated symbol. Three different modulation types are considered with pulse, square, and cosine carrier waves. In case of constraints on transmitter or receiver node it may not be possible to use pulse as a carrier, and peak-to-average messenger molecule metric is defined for this purpose. Results show that, the proposed ISI mitigation technique improves the symbol detection performance and the amplitude-based modulations are improved more than frequency-based modulations.

[1]  K. R. Harris,et al.  Diffusion in Liquids: A Theoretical and Experimental Study , 2013 .

[2]  Chan-Byoung Chae,et al.  Novel Modulation Techniques using Isomers as Messenger Molecules for Nano Communication Networks via Diffusion , 2012, IEEE Journal on Selected Areas in Communications.

[3]  Andrew W. Eckford,et al.  Tabletop Molecular Communication: Text Messages through Chemical Signals , 2013, PloS one.

[4]  Tatsuya Suda,et al.  A Biochemically-Engineered Molecular Communication System , 2009 .

[5]  Chia-han Lee,et al.  Signal detection and ISI cancellation for quantity-based amplitude modulation in diffusion-based molecular communications , 2012, 2012 IEEE Global Communications Conference (GLOBECOM).

[6]  David M Levinson,et al.  Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering , 2009, Complex.

[7]  Ian F. Akyildiz,et al.  Nanonetworks: A new communication paradigm , 2008, Comput. Networks.

[8]  Tatsuya Suda,et al.  Molecular communication through gap junction channels: System design, experiments and modeling , 2007, 2007 2nd Bio-Inspired Models of Network, Information and Computing Systems.

[9]  Özgür B. Akan,et al.  An information theoretical approach for molecular communication , 2007, 2007 2nd Bio-Inspired Models of Network, Information and Computing Systems.

[10]  Özgür B. Akan,et al.  NanoNS: A nanoscale network simulator framework for molecular communications , 2010, Nano Commun. Networks.

[11]  T. Suda,et al.  Molecular communication for nanomachines using intercellular calcium signaling , 2005, 5th IEEE Conference on Nanotechnology, 2005..

[12]  Ian F. Akyildiz,et al.  Interference effects on modulation techniques in diffusion based nanonetworks , 2012, Nano Commun. Networks.

[13]  H. T. Mouftah,et al.  On the characterization of binary concentration-encoded molecular communication in nanonetworks , 2010, Nano Commun. Networks.

[14]  Ian F. Akyildiz,et al.  Modulation Techniques for Communication via Diffusion in Nanonetworks , 2011, 2011 IEEE International Conference on Communications (ICC).

[15]  Nariman Farsad,et al.  A simple mathematical model for information rate of active transport molecular communication , 2011, 2011 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[16]  Eduard Alarcón,et al.  DIRECT: A model for molecular communication nanonetworks based on discrete entities , 2013, Nano Commun. Networks.

[17]  Massimiliano Pierobon,et al.  Diffusion-Based Noise Analysis for Molecular Communication in Nanonetworks , 2011, IEEE Transactions on Signal Processing.

[18]  Robert Schober,et al.  A Unifying Model for External Noise Sources and ISI in Diffusive Molecular Communication , 2013, IEEE Journal on Selected Areas in Communications.

[19]  Andrew W. Eckford,et al.  Symbol Interval Optimization for Molecular Communication With Drift , 2014, IEEE Transactions on NanoBioscience.

[20]  Tadashi Nakano,et al.  Molecular Communication , 2005 .

[21]  Andrew W. Eckford,et al.  Channel and Noise Models for Nonlinear Molecular Communication Systems , 2013, IEEE Journal on Selected Areas in Communications.

[22]  Raviraj S. Adve,et al.  Molecular Communication in Fluid Media: The Additive Inverse Gaussian Noise Channel , 2010, IEEE Transactions on Information Theory.

[23]  Kazuhiro Oiwa,et al.  Molecular Communication: Modeling Noise Effects on Information Rate , 2009, IEEE Transactions on NanoBioscience.

[24]  Özgür B. Akan,et al.  Body area nanonetworks with molecular communications in nanomedicine , 2012, IEEE Communications Magazine.

[25]  Eduardo José Alarcón Cot,et al.  N3Sim: A simulation framework for diffusion-based molecular communication , 2011 .

[26]  Tuna Tugcu,et al.  Energy model for communication via diffusion in nanonetworks , 2010, Nano Commun. Networks.

[27]  Massimiliano Pierobon,et al.  A physical end-to-end model for molecular communication in nanonetworks , 2010, IEEE Journal on Selected Areas in Communications.

[28]  S. Redner A guide to first-passage processes , 2001 .