Equivalent Discrete-Time Channel Modeling for Molecular Communication With Emphasize on an Absorbing Receiver

Abstract

This paper introduces the equivalent discrete-time channel model (EDTCM) to the area of diffusion-based molecular communication (DBMC). Emphasis is on an absorbing receiver, which is based on the so-called first passage time concept. In the wireless communications community the EDTCM is well known. Therefore, it is anticipated that the EDTCM improves the accessibility of DBMC and supports the adaptation of classical wireless communication algorithms to the area of DBMC. Furthermore, the EDTCM has the capability to provide a remarkable reduction of computational complexity compared to random walk based DBMC simulators. Besides the exact EDTCM, three approximations thereof based on binomial, Gaussian, and Poisson approximation are proposed and analyzed in order to further reduce computational complexity. In addition, the Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm is adapted to all four channel models. Numerical results show the performance of the exact EDTCM, illustrate the performance of the adapted BCJR algorithm, and demonstrate the accuracy of the approximations.

Channel Modeling for Diffusive Molecular Communication—A Tutorial Review

“…where for equality (a) we used lim Comparison: In order to quantify the accuracy of the Gaussian and Poisson approximations, we define the root mean square error (RMSE) between the the approximated Gaussian and Poisson CDFs, denoted by F x r (n), x ∈ {N , P}, and the Binomial CDF, denoted by F B r (n), as [91], [93], [106] In Fig. 14, the RMSE between the approximate Gaussian and Poisson CDFs and the Binomial CDF versus h(t, τ ) is shown for N tx ∈ {10 2 , 10 3 , 10 4 , 10 5 }.…”

Maximum Likelihood Detection With Ligand Receptors for Diffusion-Based Molecular Communications in Internet of Bio-Nano Things

“…Several optimal detector designs, including one-shot and sequence detectors, are proposed for this type of receiver [8], [9], [15], [16]. Recently, there emerged a different approach, which assumes that the receiver counts and absorbs every molecule that hit to its surface [17], [18]. Although the physical relevance for these types of receiver designs is not completely clear yet, they are being frequently employed in MC research to simplify the modeling and analysis.…”

“…In developing the detection methods, following the work in [16], we assume that the receiver has a finite memory, and it keeps M number of previously decoded bits in its memory to make use of them along with the channel impulse response function (18) to estimate the interference resulting from previous transmissions. Given that the sampling time is fixed and equal to t s for any signaling interval, we can write the ISI estimate of receiver in the i th signaling interval as…”

“…t s = t peak . Peak time can be derived from the impulse response (18), and depends only on the Tx-Rx distance and diffusion coefficient, i.e., t peak = d 2 6D . Additionally, we arbitrarily set the default signaling interval to T S = 4 × t peak , which leads to a moderate ISI in default setting.…”

Comparison of reception mechanisms for molecular communication via diffusion

“…A statistical-physical model for the interference in diffusion-based molecular nano networks due to molecules that are simultaneously emitted by multiple transmitting nanomachines is available in [16]. In [17], an equivalent discrete-time channel model is derived for molecular communication via diffusion based on the characteristic function with emphasis on an absorbing receiver.…”