Realistic models of the components and processes are required for molecular communication (MC) systems. In this paper, a spheroidal receiver structure is proposed for MC that is inspired by the 3D cell cultures known as spheroids being widely used in organ-on-chip systems. A simple diffusive MC system is considered where the spheroidal receiver and a point source transmitter are in an unbounded fluid environment. The spheroidal receiver is modeled as a porous medium for diffusive signaling molecules, then its boundary conditions and effective diffusion coefficient are characterized. It is revealed that the spheroid amplifies the diffusion signal, but also disperses the signal which reduces the information communication rate. Furthermore, we analytically formulate and derive the concentration Green's function inside and outside the spheroid in terms of infinite series-forms that are confirmed by a particle-based simulator (PBS).

Channel modeling is a challenging vital step towards the development of diffusion-based molecular communication networks (DMCNs). Analytical approaches for diffusion channel modeling are limited to simple and specific geometries and boundary conditions. Also, simulation- and experiment-driven methods are very time-consuming and computationally complex. In this paper, the channel model for DMCN employing the fundamental concentration Green's function (CGF) is characterized. A general homogeneous boundary condition framework is considered that includes any linear reaction systems at the boundaries in the environment. To obtain the CGF for a general DMCN including multiple transmitters, receivers, and other objects with arbitrary geometries and boundary conditions, a semi-analytical method (SAM) is proposed. The CGF linear integral equation (CLIE) is analytically derived. By employing the numerical method of moments, the problem of CGF derivation from CLIE is transformed into an inverse matrix problem. Moreover, a sequential SAM is proposed that converts the inversion problem of a large matrix into multiple smaller matrices reducing the computational complexity. Particle-based simulator confirms the results obtained from the proposed SAM. The convergence and run time for the proposed method are examined. Further, the error probability of a simple diffusion-based molecular communication system is analyzed and examined using the proposed method.

Diffusive molecular communication (DMC) is envisioned as a promising approach to help realize healthcare applications within bounded biological environments. In this paper, a DMC system within a biological spherical environment (BSE) is considered, inspired by bounded biological sphere-like structures throughout the body. As a biological environment, it is assumed that the inner surface of the sphere's boundary is fully covered by biological receptors that may irreversibly react with hitting molecules. Moreover, information molecules diffusing in the sphere may undergo a degradation reaction and be transformed to another molecule type. Concentration Green's function (CGF) of diffusion inside this environment is analytically obtained in terms of a convergent infinite series. By employing the obtained CGF, the information channel between transmitter and transparent receiver of DMC in this environment is characterized. Interestingly, it is revealed that the information channel is reciprocal, i.e., interchanging the position of receiver and transmitter does not change the information channel. Results indicate that the conventional simplifying assumption that the environment is unbounded may lead to an inaccurate characterization in such biological environments.

Diffusion-based molecular communication (DMC) is one of the most promising approaches for realizing nano-scale communications for healthcare applications. The DMC systems in in-vivo environments may encounter biological entities that release molecules identical to the molecules used for signaling as part of their functionality. Such entities in the environment act as external noise sources from the DMC system's perspective. In this paper, the release of molecules by external bio-inspired noise sources is particularly modeled as a compound Poisson process. The impact of compound Poisson noise sources (CPNSs) on the performance of a point-to-point DMC system is investigated. To this end, the noise from the CPNS observed at the receiver is characterized. Considering a simple on-off keying modulation and formulating symbol-by-symbol maximum likelihood (ML) detector, the performance of the DMC system in the presence of the CPNS is analyzed. For the special case of CPNS in a high-rate regime, the noise received from the CPNS is approximated as a Poisson process whose rate is normally distributed. In this case, it is proved that a simple single-threshold detector is an optimal ML detector. Our results reveal that in general, adopting the conventional simple homogeneous Poisson noise model may lead to overly optimistic performance predictions, if a CPNS is present.

In this paper, we consider the diffusive molecular communication channel between a transmitter nano-machine and a receiver nano-machine in a fluid environment. The information molecules released by the transmitter nano-machine into the environment can degrade in the channel via a first-order degradation reaction and those that reach the receiver nano-machine can participate in a reversible bimolecular-reaction with receiver receptor proteins. We derive a closed-form analytical expression for the expected received signal at the receiver, i.e., the expected number of activated receptors on the surface of the receiver. The accuracy of the derived analytical result is verified with a Brownian motion particle-based simulation of the environment.

The LTI-Poisson model is a natural extension of the conventional memoryless Poisson channel to include memory, and can model the ISI effect in diffusion based molecular communication networks. In this paper, we exploit prior art on linear ISI channels to provide a computable finite-letter characterization of the capacity of single-hop LTI-Poisson networks. Then we find more explicit single-letter lower and upper bounds on the capacity in the point to point case. Further, an approach for bounding mutual information in the low SNR regime using the symmetrized KL divergence is introduced and its applicability for Poisson channels is demonstrated. This leads to a non-trivial upper bound on the capacity of Poisson channel with a maximum transmission constraint in the low SNR regime, which to best of our knowledge is the first such bound.

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-bit 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 computed threshold. 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 multi-read 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.