Recent studies show that real-time precise user localization enables to deliver accurate beamforming in MIMO systems without the need for channel estimation. This paper presents new solutions for accurate user localization in massive MIMO LTE systems. A key novelty of the developed schemes is the ability to locate users during LTE's random access channel synchronization procedure before they are connected to the network, by which the obtained location information can be immediately used to optimize the allocation of radio resource and perform accurate beamforming. To achieve this, the developed solutions leverage the advantages of spherical wave propagation since it allows simultaneously estimating the angle of arrival and the propagation distance from the user equipment to each antenna element in the base station. We design solutions for both single-path line-of-sight communication and multi-path propagation environments. The developed schemes were evaluated through both simulations and proof-of-concept experiments. Simulation results show that both algorithms can achieve decimeter-level localization accuracy using 64 and more antenna elements for the distances up to 300 meters. The proofof-concept experiment justifies the feasibility of user localization based on the estimation of the shape of the incoming wavefront.

A platoon comprises a string of consecutive highly automated vehicles traveling together. Platooning allows for increased road utilization and reduced fuel consumption due to short inter-vehicular distances. Safety in terms of guaranteeing no rear-end collisions is of utmost importance for platooning systems to be deployed in practice.We compare how safely emergency braking can be handled by emerging vehicle-to-vehicle (V2V) communications on the one hand and by radar-based measurements of existing automatic emergency braking systems (AEBS) on the other. We show that even under conservative assumptions on the V2V communications, such an approach significantly outperforms AEBS with an ideal radar sensor in terms of allowed inter-vehicle distances and response times.Furthermore, we design two emergency braking strategies for platooning based on V2V communications. The first braking strategy assumes centralized coordination by the leading vehicle and exploits necessary optimal conditions of a constrained optimization problem, whereas the second -the more conservative solution -assumes only local information and is distributed in nature. Both strategies are also compared with the AEBS.

For high-resolution massive MIMO and very large antenna arrays, wireless channel models have to scrutinize the detailed space features of the surrounding environment. Existing models such as WINNER and 3GPP are not appropriate for validating and evaluating new concepts for 4G/5G as they do not consider the spatial characteristics of the real environment. Several geometry-based channel models have been proposed by exploiting ray tracing and considering simplified 3D shapes of buildings and objects modeled using only vertical and horizontal planes. However, the channel in these models may significantly differ from the real channel due to the inaccuracy of the object shapes. In this paper, we present an approach to model the specular reflection of a signal from an arbitrary inclined surface as well as the change of signal polarizations. We further use this approach to simulate MIMO antennas. The proposed scheme was validated through simulating LTE uplink transmissions in a real environment modeled based on Google Maps. Results showed the importance of considering detailed 3D characteristics of the surroundings in simulations. We observed that even slightly inclined walls can have significant influence on channels in comparison with models with only vertical and horizontal surfaces due to different propagation paths, different angles of reflection, and different changes of polarizations.

Formal models for the safety validation of autonomous vehicles have become increasingly important. To this end, we present a safety framework for longitudinal automated driving. This framework allows calculating minimum safe intervehicular distances for arbitrary ego vehicle control policies. We use this framework to enhance the Responsibility-Sensitive Safety (RSS) model and models based on it, which fail to cover situations where the ego vehicle has a higher decelerating capacity than its preceding vehicle. For arbitrary ego vehicle control policies, we show how our framework can be applied by substituting real (possibly computationally intractable) controllers with upper bounding functions. This comprises a general approach for longitudinal safety, where safety guarantees for the upper-bounded system are equivalent to those for the original system but come at the expense of larger inter-vehicular distances.