Two new papers on molecular communication

I just got back from lovely Cambridge, Mass., where I managed to sneak in a day to see some of the interesting talks at Bionetics before storming back to Toronto to cover my teaching schedule.  December in Boston, what more could you ask for?

I'm a coauthor on a couple of new molecular communication papers that just came out, one at Bionetics, and the other on arXiv:

• For Bionetics, my Ph.D. student, Nariman Farsad, and our collaborators, Satoshi Hiyama and Yuki Moritani of NTT DOCOMO, continued some of our earlier work on microchannels.  This was a short "work-in-progress" paper, where we considered the performance of active transport systems in a more realistic simulation environment.  Surprisingly, Brownian motion with drift usually does better than molecular motors -- probably because every molecule can start propagating right away, rather than waiting to be picked up by a motor. [N. Farsad, A. W. Eckford, S. Hiyama, and Y. Moritani, "Information rates of active propagation in microchannel molecular communication,” in Proc. 5th International Conference on Bio-Inspired Models of Network, Information, and Computing Systems, Boston, MA, USA, 2010. (pdf)]

• On arXiv (and also submitted to Trans. Info. Theory), K. V. Srinivas, Ravi Adve, and I extended Sachin's earlier work on Brownian motion with drift.  We're looking at molecular timing channels, where information is encoded in the transmission time x. The receiver sees the reception time y, where y = x + n, and where n is the first arrival time of a Brownian motion; thus, these are additive noise channels. It turns out that the first arrival time distribution of a Brownian motion with positive drift is given by the inverse Gaussian distribution (kind of an odd name at first glance, since it's not the reciprocal of a Gaussian PDF).  So a molecular timing channel in the presence of positive drift is an additive inverse Gaussian noise channel. There is a rich literature on the inverse Gaussian, which lets us get closed-form solutions and bounds for a number of important quantities, like ML detectors, probabilities of error, and capacities -- sadly, not as clean and nice as the solutions for the AWGN, but we do what we can. [K. V. Srinivas, R. S. Adve, and A. W. Eckford, "Molecular communication in fluid media: The additive inverse Gaussian noise channel," arXiv:1012.0081 (submitted to IEEE Transactions on Information Theory).]

You can also check out more of my molecular communication work here.