Receivers for Diffusion-Based Molecular Communication: Exploiting Memory and Sampling Rate

Abstract

In this paper, a diffusion-based molecular communication channel between two nano-machines is considered. The effect of the amount of memory on performance is characterized, and a simple memory-limited decoder is proposed; its performance is shown to be close to that of the best possible decoder (without any restrictions on the computational complexity or its functional form), using Genie-aided upper bounds. This effect is adapted to the case of Molecular Concentration Shift Keying; it is shown that a four-bits memory achieves nearly the same performance as infinite memory for all of the examples considered. A general class of threshold decoders is considered and shown to be suboptimal for a Poisson channel with memory, unless the SNR is higher than a value specified in the paper. During each symbol duration (symbol period), the probability that a released molecule hits the receiver, changes over the duration of the period; thus, we also consider a receiver that samples at a rate higher than the transmission rate (a multiread system). A multi-read system improves performance. The associated decision rule for this system is shown to be a weighted sum of the samples during each symbol interval. The performance of the system is analyzed using the saddle point approximation. The best performance gains are achieved for an oversampling factor of three for the examples considered.

Performance Analysis of Diffusion-Based Molecular Communications With Memory

“…By using F b (t) in (14), the probability that symbol S b is delivered to the RN within the current slot can be introduced as F b (τ ). Now, we can write the probability that symbol S a is the only symbol which is successfully delivered to the RN as follows:…”

“…Therefore, the effects of ISI through arbitrary level of memory cannot be discussed in this work. In [13] and [14], a Poisson model is used for the flow of molecules exiting the storage and diffusing in the environment. In order to take into account the overall effect of previous time slots, a compound Poisson process is derived as a weighted sum of the Poisson processes corresponding to previous slots.…”

A Novel Pre-Equalization Method for Molecular Communication via Diffusion in Nanonetworks

“…Equation (5) indicates that µ {i} is dependent on the previously transmitted bits, as well as the current transmission. Notice that (5), which is frequently used in the literature [10], is indeed a discrete convolution, and can be used to derive the coefficients of the FIR filter that models the diffusion channel. Let h k denote the coefficients of this FIR filter of length K, which can be calculated as…”

An optimal decoding algorithm for Molecular Communications systems with noise, memory, and pulse width

“…Such information can be expressed by either the number of certain molecules or the molecular concentration. In the first case, in [1,2,3,4], researchers focused on the movement of individual molecules. There exists a certain probability for diffusing molecules to be captured by the RN, and the capture probability is utilised to describe the propagation mechanism.…”

“…In the second category, the decoding threshold varies depending on previously received symbols. In [4], the value of the threshold is designed to maximise a posteriori probability, but the system model should be refined by considering the emission effect. Other research such as [18,19] has taken the emission process into account, and the threshold changes with regards to the previously decoded bits.…”