Bounds on the Capacity of ASK Molecular Communication Channels with ISI

There are now several works on the use of the additive inverse Gaussian noise (AIGN) model for the random transit time in molecular communication (MC) channels. The randomness invariably causes inter- symbol interference (ISI) in MC, an issue largely ignored or simplified. In this paper we derive an upper bound and two lower bounds for MC based on amplitude shift keying (ASK) in presence of ISI. The Blahut-Arimoto algorithm (BAA) is modified to find the input distribution of transmitted symbols to maximize the lower bounds. Our results show that over wide parameter values the bound are close.

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

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

[3]  Hui Li,et al.  Capacity of the Memoryless Additive Inverse Gaussian Noise Channel , 2014, IEEE Journal on Selected Areas in Communications.

[4]  Wei Zeng,et al.  Simulation-Based Computation of Information Rates for Channels With Memory , 2006, IEEE Transactions on Information Theory.

[5]  Ian F. Akyildiz,et al.  A diffusion-based binary digital communication system , 2012, 2012 IEEE International Conference on Communications (ICC).

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

[7]  Giorgio Taricco,et al.  An asymptotic approximation of the ISI channel capacity , 2014, 2014 Information Theory and Applications Workshop (ITA).

[8]  Richard E. Blahut,et al.  Computation of channel capacity and rate-distortion functions , 1972, IEEE Trans. Inf. Theory.

[9]  Kyung Sup Kwak,et al.  Molecular nanonetwork channel model , 2013 .

[10]  Shlomo Shamai,et al.  Information rates for a discrete-time Gaussian channel with intersymbol interference and stationary inputs , 1991, IEEE Trans. Inf. Theory.

[11]  Ranjan K. Mallik,et al.  Molecular communication with Brownian motion and a positive drift: performance analysis of amplitude modulation schemes , 2014, IET Commun..

[12]  Shlomo Shamai,et al.  The intersymbol interference channel: lower bounds on capacity and channel precoding loss , 1996, IEEE Trans. Inf. Theory.

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

[14]  James L. Massey,et al.  Capacity of the discrete-time Gaussian channel with intersymbol interference , 1988, IEEE Trans. Inf. Theory.

[15]  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.

[16]  Hans-Andrea Loeliger,et al.  A Generalization of the Blahut–Arimoto Algorithm to Finite-State Channels , 2008, IEEE Transactions on Information Theory.