Improving reliability performance of diffusion-based molecular communication with adaptive threshold variation algorithm

In this work, we investigate the communication reliability for diffusion-based molecular communication, using the indicator of bit error rate (BER). A molecular classified model is established to divide molecules into three parts, which are the signal, inter-symbol interference (ISI) and noise. We expand each part separately using molecular absorbing probability, and connect them by a traditional-like formula. Based on the classified model, we do a theoretical analysis to prove the feasibility of improving the BER performance. Accordingly, an adaptive threshold variation (ATV) algorithm is designed in demodulation to implement the goal, which makes the receiver adapt the channel condition properly through learning process. Moreover, the complexity of ATV is calculated and its performance in various noisy channel is discussed. An expression of Signal to Interference plus Noise Ratio (SINR) is defined to verify the system performance. We test some important parameters of the channel model, as well as the ATV algorithm in the simulation section. The results have shown the performance gain of the proposal.

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

[2]  Matthew D. Higgins,et al.  Forward error correction for molecular communications , 2012, Nano Commun. Networks.

[3]  J. Aylott.  Optical nanosensors--an enabling technology for intracellular measurements. , 2003, The Analyst.

[4]  O. B. Akan,et al.  Nanoscale Communication With Molecular Arrays in Nanonetworks , 2012, IEEE Transactions on NanoBioscience.

[5]  Adnan Aijaz,et al.  Error Performance of Diffusion-Based Molecular Communication Using Pulse-Based Modulation , 2015, IEEE Transactions on NanoBioscience.

[6]  Siyi Wang,et al.  Low-Complexity Noncoherent Signal Detection for Nanoscale Molecular Communications , 2016, IEEE Transactions on NanoBioscience.

[7]  Jaime Lloret,et al.  A survey of IEEE 802.15.4 effective system parameters for wireless body sensor networks , 2016, Int. J. Commun. Syst..

[8]  Massimiliano Pierobon,et al.  Intersymbol and co-channel interference in diffusion-based molecular communication , 2012, 2012 IEEE International Conference on Communications (ICC).

[9]  Olle Inganäs,et al.  Polymer Hydrogel Microelectrodes for Neural Communication , 2002 .

[10]  LiuQiang,et al.  Channel capacity analysis of a diffusion-based molecular communication system with ligand receptors , 2015 .

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

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

[13]  Tuna Tugcu,et al.  ISI Mitigation Techniques in Molecular Communication , 2014, IEEE Transactions on Molecular, Biological and Multi-Scale Communications.

[14]  Richard J. Lipton,et al.  Fidelity of Enzymatic Ligation for DNA Computing , 2000, J. Comput. Biol..

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

[16]  Do-Un Jeong,et al.  Empirical analysis of the reliability of low-rate wireless u-healthcare monitoring applications , 2013, Int. J. Commun. Syst..

[17]  Qiang Liu,et al.  Channel capacity analysis of a diffusion‐based molecular communication system with ligand receptors , 2015, Int. J. Commun. Syst..

[18]  Ian F. Akyildiz,et al.  Bacteria-based communication in nanonetworks , 2010, Nano Commun. Networks.

[19]  Massimiliano Pierobon,et al.  Information capacity of diffusion-based molecular communication in nanonetworks , 2011, 2011 Proceedings IEEE INFOCOM.

[20]  Kwang-Cheng Chen,et al.  On the asynchronous information embedding for event-driven systems in molecular communications , 2013, Nano Commun. Networks.

[21]  Kyung Sup Kwak,et al.  Effect of memory on BER in molecular communication , 2014 .

[22]  K. S. Deepak,et al.  Improving energy efficiency of incremental relay based cooperative communications in wireless body area networks , 2015, Int. J. Commun. Syst..

[23]  Ian F. Akyildiz,et al.  Antibody-based molecular communication for targeted drug delivery systems , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[24]  Rainer Malaka,et al.  Kinetic models of odor transduction implemented as artificial neural networks , 1995, Biological Cybernetics.

[25]  Özgür B. Akan,et al.  Mobile Ad Hoc Nanonetworks with Collision-Based Molecular Communication , 2012, IEEE Transactions on Mobile Computing.

[26]  Qiang Liu,et al.  Multiple-access channel capacity of diffusion and ligand-based molecular communication , 2013, MSWiM.

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

[28]  Urbashi Mitra,et al.  Adaptive molecule transmission rate for diffusion based molecular communication , 2015, 2015 IEEE International Conference on Communications (ICC).

[29]  Özgür B. Akan,et al.  Receiver Design for Molecular Communication , 2013, IEEE Journal on Selected Areas in Communications.

[30]  K. Sneppen,et al.  Theoretical Analysis of Epigenetic Cell Memory by Nucleosome Modification , 2007, Cell.

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

[32]  Massimiliano Pierobon,et al.  A Molecular Communication System Model for Particulate Drug Delivery Systems , 2013, IEEE Transactions on Biomedical Engineering.

[33]  Tadashi Nakano,et al.  Channel Model and Capacity Analysis of Molecular Communication with Brownian Motion , 2012, IEEE Communications Letters.

[34]  Robert Schober,et al.  Improving Receiver Performance of Diffusive Molecular Communication With Enzymes , 2013, IEEE Transactions on NanoBioscience.

[35]  Andrew W. Eckford,et al.  A Markov Chain Channel Model for Active Transport Molecular Communication , 2014, IEEE Transactions on Signal Processing.

[36]  Jongyoon Han,et al.  Molecular sieving using nanofilters: past, present and future. , 2008, Lab on a chip.

[37]  Robert Langer,et al.  Impact of nanotechnology on drug delivery. , 2009, ACS nano.

[38]  Qiang Liu,et al.  Adaptive Threshold Variation Algorithm for Diffusion-based Molecular Communication , 2015, NANOCOM.

[39]  Tuna Tugcu,et al.  A Novel Pre-Equalization Method for Molecular Communication via Diffusion in Nanonetworks , 2015, IEEE Communications Letters.

[40]  Takahiro Hara,et al.  Cooperative Target Tracking by a Mobile Bionanosensor Network , 2014, IEEE Transactions on NanoBioscience.

[41]  Cristina Comaniciu,et al.  Cross-layer MAC design for location-aware wireless sensor networks , 2011, Int. J. Commun. Syst..