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 well-fitting 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.

Besides mimicking bio-chemical and multi-scale communication mechanisms, molecular communication forms a theoretical framework for virus infection processes. Towards this goal, aerosol and droplet transmission has recently been modeled as a multiuser scenario. In this letter, the “infection performance” is evaluated by means of a mutual information analysis, and by an even simpler probabilistic performance measure which is closely related to absorbed viruses. The so-called infection rate depends on the distribution of the channel input events as well as on the transition probabilities between channel input and output events. The infection rate is investigated analytically for five basic discrete memoryless channel models. Numerical results for the transition probabilities are obtained by Monte Carlo simulations for pathogen-laden particle transmission in four typical indoor environments: two-person office, corridor, classroom, and bus. Particle transfer contributed significantly to infectious diseases like SARS-CoV-2 and influenza.

Current research focuses on radio frequency (RF) systems for autonomous driving and urban air mobility, sharing demands for utmost reliable communication and radar systems installed in confined spaces. As a result, co-existence considerations are becoming increasingly important. By dividing antenna groups into sub-groups and increasing their spacing, mutual interference can be reduced. In our research, we seek to reduce RF interference effects in medium-size autonomous aerial vehicles (AAVs). To this end, distributed MIMO radar arrays are employed to reduce out-of-band interference effects between the radar and other on-board RF systems, while at the same time offering angular diversity for better detectability of fluctuating targets. To solve the issue of angular ambiguity associated with distributed arrays, we combine our setup with compressed sensing-based DoA estimation, while optimizing the sensing matrix such that it matches the resulting grating lobe structure. Our results show that the proposed bistatic MIMO setup is able to outperform monostatic setups for targets with angle-dependent radar cross sections. The first part of the paper reviews relevant radar signal and array processing techniques in a survey style. Based on this, it then provides an analytical framework to explain, how these techniques are combined to attain our goals.

To enable a safe and reliable operation in future urban air mobility scenarios with a large number of autonomous aerial vehicles, automatic sensing systems will be required during the starting and landing phase as well as through comparatively narrow air spaces typical for urban areas. This paper shows an implementation of a compressive sensing (CS) based obstacle-detection and direction-of-arrival (DoA) estimation approach. To this end, a multiple-input multiple-output frequency-modulated continuous wave radar system with two transmit and four receive antenna elements is employed to sense the environment and provide situational awareness from a virtual aperture with eight antennas. Example measurements result from a typical urban air setting with a-priori unknown sparsity. The results show that greater signal dynamics are attained from CS compared to conventional beamforming based DoA estimation techniques. Furthermore, measurements are verified by corresponding time-synchronous lidar measurements. CS-based obstacle detection and DoA estimation is shown to work even in complex measurement environments.

The anticipated trends in mobility, either autonomous driving or urban air mobility (UAM), require wireless services to achieve mandatory reliability and safety levels. The number of wireless systems already in-use, especially airborne, occupy regulated spectra. In order to mitigate the impact of a large number of systems mounted to airborne platforms, we propose to combine enabling techniques. These include the joint waveform design, multi-mode multi-port antennas (M ³ PAs), and appropriate beamforming techniques for joint communication, sensing and localization (JCSL). Our study finds that employing DFT-spread OFDM results in a more consistent radar performance compared to conventional OFDM, highlighting a novel application of this waveform design in JCSL systems. M³PAs are explored as a candidate system for performing JCSL using a single antenna radiator. It is shown how the orthogonality properties of M ³ PAs are beneficial for avoiding crosstalk in JCSL. We therefore review mature and actively employed techniques and discuss the applicability of M ³ PAs and novel waveform designs for JCSL. This study aims to conduct a thorough analysis of current systems in operation. Furthermore, design concepts that facilitate the implementation of the JCSL concept in UAM and airborne applications are introduced and discussed.

Two‐dimensional direction‐of‐arrival (DoA) estimation in azimuth and elevation via radar systems equipped with uniform rectangular arrays (URAs) will play an important role in various application areas—most distinctively in future urban air mobility settings with unmanned aerial vehicles. A key factor is the fast and reliable provision of target detections in terms of range and DoA for safe autonomous operation of the vehicle using on‐board antenna arrays with compact installation size. The authors present a technique for improving the performance of DoA estimation using compressive sensing in conjunction with multiple‐input multiple‐output arrays with electronically steered beams in the transmit direction. The simulation study investigates the impact of different design considerations on radar signal processing performance. An optimisation of a radar system using electronic beamsteering in the transmit domain is presented numerically. Based on the architecture of the URAs used, performance and detection accuracy can be improved.