Potentials of Deterministic Radio Propagation Simulation for AI-Enabled Localization and Sensing

Machine leaning (ML) and artificial intelligence (AI) enable new methods for localization and sensing in next-generation networks to fulfill a wide range of use cases. These approaches rely on learning approaches that require large amounts of training and validation data. This paper addresses the data generation bottleneck to develop and validate such methods by proposing an integrated toolchain based on deterministic channel modeling and radio propagation simulation. The toolchain is demonstrated exemplary for scenario classification to obtain localization-related channel parameters within an aircraft cabin environment

Toward UWB Impulse Radio Sensing: Fundamentals, Potentials, and Challenges

Radio sensing is a rapidly emerging research field. It focuses on designing an integrated communication system that can also perform localization and radar functionalities sharing the same transmit signals and potentially the same hardware. Ultra-wideband (UWB) impulse radio is a promising technology for radio sensing because it offers a high-range resolution and direct access to the channel impulse response (CIR) to observe the multipath components (MPCs) of the wideband channel caused by scattering at target objects. This approach enables a wide range of functionalities and applications, especially in the field of mobility and transportation. The foundation is given by the signal propagation and channel modeling of the UWB channel, which is briefly revisited in this chapter. Based on the CIR and estimated MPCs the target object can be localized like a multistatic passive radar. The influence of geometry in a passive target localization system is studied by calculating the geometric dilution of precision (GDOP). In addition to passive localization more tasks and functionalities of radio sensing, are briefly introduced including detection, tracking, imaging, counting, and classification. The chapter concludes with further research directions and challenges in UWB radio sensing, especially for real-world use in the context of mobility applications

Lab-Based Evaluation of Device-Free Passive Localization Using Multipath Channel Information

The interconnection of devices, driven by the Internet of Things (IoT), enables a broad variety of smart applications and location-based services. The latter is often realized via transponder based approaches, which actively determine device positions within Wireless Sensor Networks (WSN). In addition, interpreting wireless signal measurements also enables the utilization of radar-like passive localization of objects, further enhancing the capabilities of WSN ranging from environmental mapping to multipath detection. For these approaches, the target objects are not required to hold any device nor to actively participate in the localization process. Instead, the signal delays caused by reflections at objects within the propagation environment are used to localize the object. In this work, we used Ultra-Wide Band (UWB) sensors to measure Channel Impulse Responses (CIRs) within a WSN. Determining an object position based on the CIR can be achieved by formulating an elliptical model. Based on this relation, we propose a CIR environmental mapping (CIR-EM) method, which represents a heatmap generation of the propagation environment based on the CIRs taken from radio communication signals. Along with providing imaging capabilities, this method also allows a more robust localization when compared to state-of-the-art methods. This paper provides a proof-of-concept of passive localization solely based on evaluating radio communication signals by conducting measurement campaigns in an anechoic chamber as a best-case environment. Furthermore, shortcomings due to physical layer limitations when using non-dedicated hardware and signals are investigated. Overall, this work lays a foundation for related research and further evaluation in more application-oriented scenarios

Carbohydrate-Lectin Recognition of Sequence-Defined Heteromultivalent Glycooligomers

Multivalency as a key principle in nature has been successfully adopted for the design and synthesis of artificial glycoligands by attaching multiple copies of monosaccharides to a synthetic scaffold. Besides their potential in various applied areas, e.g. as antiviral drugs, for the vaccine development and as novel biosensors, such glycomimetics also allow for a deeper understanding of the fundamental aspects of multivalent binding of both artificial and natural ligands. However, most glycomimetics so far neglect the purposeful arranged heterogeneity of their natural counterparts, thus limiting more detailed insights into the design and synthesis of novel glycomimetics. Therefore, this work presents the synthesis of monodisperse glycooligomers carrying different sugar ligands at well-defined positions along the backbone using for the first time sequential click chemistry and stepwise assembly of functional building blocks on solid support. This approach allows for straightforward access to sequence-defined, multivalent glycooligomers with full control over number, spacing, position, and type of sugar ligand. We demonstrate the synthesis of a set of heteromultivalent oligomers presenting mannose, galactose, and glucose residues. All heteromultivalent structures show surprisingly high affinities toward Concanavalin A lectin receptor in comparison to their homomultivalent analogues presenting the same number of binding ligands. Detailed studies of the ligand/receptor interaction using STD-NMR and 2fFCS indeed indicate a change in binding mechanism for trivalent glycooligomers presenting mannose or combinations of mannose and galactose residues. We find that galactose residues do not participate in the binding to the receptor, but they promote steric shielding of the heteromultivalent glycoligands and thus result in an overall increase in affinity. Furthermore, the introduction of nonbinding ligands seems to suppress receptor clustering of multivalent ligands. Overall these results support the importance of heteromultivalency specifically for the design of novel glycoligands and help to promote a fundamental understanding of multivalent binding modes