Channel Coding in Molecular Communication

This dissertation establishes and analyzes a complete molecular transmission system from a communication engineering perspective. Its focus is on diffusion-based molecular communication in an unbounded three-dimensional fluid medium. As a basis for the investigation of transmission algorithms, an equivalent discrete-time channel model (EDTCM) is developed and the characterization of the channel is described by an analytical derivation, a random walk based simulation, a trained artificial neural network (ANN), and a proof of concept testbed setup. The investigated transmission algorithms cover modulation schemes at the transmitter side, as well as channel equalizers and detectors at the receiver side. In addition to the evaluation of state-of-the-art techniques and the introduction of orthogonal frequency-division multiplexing (OFDM), the novel variable concentration shift keying (VCSK) modulation adapted to the diffusion-based transmission channel, the lowcomplex adaptive threshold detector (ATD) working without explicit channel knowledge, the low-complex soft-output piecewise linear detector (PLD), and the optimal a posteriori probability (APP) detector are of particular importance and treated. To improve the error-prone information transmission, block codes, convolutional codes, line codes, spreading codes and spatial codes are investigated. The analysis is carried out under various approaches of normalization and gains or losses compared to the uncoded transmission are highlighted. In addition to state-of-the-art forward error correction (FEC) codes, novel line codes adapted to the error statistics of the diffusion-based channel are proposed. Moreover, the turbo principle is introduced into the field of molecular communication, where extrinsic information is exchanged iteratively between detector and decoder. By means of an extrinsic information transfer (EXIT) chart analysis, the potential of the iterative processing is shown and the communication channel capacity is computed, which represents the theoretical performance limit for the system under investigation. In addition, the construction of an irregular convolutional code (IRCC) using the EXIT chart is presented and its performance capability is demonstrated. For the evaluation of all considered transmission algorithms the bit error rate (BER) performance is chosen. The BER is determined by means of Monte Carlo simulations and for some algorithms by theoretical derivation

Duality between Coronavirus Transmission and Air-based Macroscopic Molecular Communication

This contribution exploits the duality between a viral infection process and macroscopic air-based molecular communication. Airborne aerosol and droplet transmission through human respiratory processes is modeled as an instance of a multiuser molecular communication scenario employing respiratory-event-driven molecular variable-concentration shift keying. Modeling is aided by experiments that are motivated by a macroscopic air-based molecular communication testbed. In artificially induced coughs, a saturated aqueous solution containing a fluorescent dye mixed with saliva is released by an adult test person. The emitted particles are made visible by means of optical detection exploiting the fluorescent dye. The number of particles recorded is significantly higher in test series without mouth and nose protection than in those with a wellfitting medical mask. A simulation tool for macroscopic molecular communication processes is extended and used for estimating the transmission of infectious aerosols in different environments. Towards this goal, parameters obtained through self experiments are taken. The work is inspired by the recent outbreak of the coronavirus pandemic.Comment: 9 pages, 6 figures, submitted to IEEE Transactions on Molecular, Biological, and Multi-Scale Communications for the special issue "Section II: Molecular Communications for Diagnostics and Therapeutic Development of Infectious Diseases

Invited Review: Decoding the pathophysiological mechanisms that underlie RNA dysregulation in neurodegenerative disorders: a review of the current state of the art

Altered RNA metabolism is a key pathophysiological component causing several neurodegenerative diseases. Genetic mutations causing neurodegeneration occur in coding and noncoding regions of seemingly unrelated genes whose products do not always contribute to the gene expression process. Several pathogenic mechanisms may coexist within a single neuronal cell, including RNA/protein toxic gain-of-function and/or protein loss-of-function. Genetic mutations that cause neurodegenerative disorders disrupt healthy gene expression at diverse levels, from chromatin remodelling, transcription, splicing, through to axonal transport and repeat-associated non-ATG (RAN) translation. We address neurodegeneration in repeat expansion disorders [Huntington's disease, spinocerebellar ataxias, C9ORF72-related amyotrophic lateral sclerosis (ALS)] and in diseases caused by deletions or point mutations (spinal muscular atrophy, most subtypes of familial ALS). Some neurodegenerative disorders exhibit broad dysregulation of gene expression with the synthesis of hundreds to thousands of abnormal messenger RNA (mRNA) molecules. However, the number and identity of aberrant mRNAs that are translated into proteins – and how these lead to neurodegeneration – remain unknown. The field of RNA biology research faces the challenge of identifying pathophysiological events of dysregulated gene expression. In conclusion, we discuss current research limitations and future directions to improve our characterization of pathological mechanisms that trigger disease onset and progression

Piecewise linear detection for direct superposition modulation

Considering high-order digital modulation schemes, the bottleneck in consumer products is the detector rather than the modulator. The complexity of the optimal a posteriori probability (APP) detector increases exponentially with respect to the number of modulated bits per data symbol. Thus, it is necessary to develop low-complexity detection algorithms with an APP-like performance, especially when performing iterative detection, for example in conjunction with bit interleaved coded modulation. We show that a special case of superposition modulation, dubbed Direct Superposition Modulation (DSM), is particularly suitable for complexity reduction at the receiver side. As opposed to square QAM, DSM achieves capacity without active signal shaping. The main contribution is a low-cost detection algorithm for DSM, which enables iterative detection by taking a priori information into account. This algorithm exploits the approximate piecewise linear behavior of the soft outputs of an APP detector over the entire range of detector input values. A theoretical analysis and simulation results demonstrate that at least max-log APP performance can be reached, while the complexity is significantly reduced compared to classical APP detection. Keywords: Digital modulation, Demodulation, Detection algorithms, Linear approximatio

Symbol detection based on Voronoi surfaces with emphasis on superposition modulation

A challenging task when applying high-order digital modulation schemes is the complexity of the detector. Particularly, the complexity of the optimal a posteriori probability (APP) detector increases exponentially with respect to the number of bits per data symbol. This statement is also true for the Max-Log-APP detector, which is a common simplification of the APP detector. Thus it is important to design new detection algorithms which combine a sufficient performance with low complexity. In this contribution, a detection algorithm for two-dimensional digital modulation schemes which cannot be split-up into real and imaginary parts (like phase shift keying and phase-shifted superposition modulation (PSM)) is proposed with emphasis on PSM with equal power allocation. This algorithm exploits the relationship between Max-Log-APP detection and a Voronoi diagram to determine planar surfaces of the soft outputs over the entire range of detector input values. As opposed to state-of-the-art detectors based on Voronoi surfaces, a priori information is taken into account, enabling iterative processing. Since the algorithm achieves Max-Log-APP performance, even in the presence of a priori information, this implies a great potential for complexity reduction compared to the classical APP detection

Infectious Disease Transmission via Aerosol Propagation from a Molecular Communication Perspective : Shannon Meets Coronavirus

Molecular communication is not only able to mimic biological and chemical communication mechanisms, but also provides a theoretical framework for viral infection processes. In this tutorial, aerosol and droplet transmission is modeled as a multiuser scenario with mobile nodes, related to broadcasting and relaying. In contrast to data communication systems, in the application of pathogen-laden aerosol transmission, mutual information between nodes should be minimized. Towards this goal, several countermeasures are reasoned. The findings are supported by experimental results and by an advanced particle simulation tool. This work is inspired by the recent outbreak of the coronavirus (COVID-19) pandemic, but also applicable to other airborne infectious diseases like influenza.Comment: 7 pages, 4 figures, 1 table, submitted to IEEE Communications Magazine for the Feature Topic on "Nano-Networking for Nano-, Micro-, and Macro-Scale Applications