Assisted living (AL) technologies, enabled by technical advances such as the advent of the Internet-of-Things, are increasingly gaining importance in our ageing society. This article discusses the potential of future high-accuracy localization systems as a key component of AL applications. Accurate location information can be tremendously useful to realize, e.g., behavioral monitoring, fall detection, and real-time assistance. Such services are expected to provide older adults and people with disabilities with more independence and thus to reduce the cost for caretaking.Total cost of ownership and ease of installation are paramount to make sensor systems for AL viable. In case of a radiobased indoor localization system, this implies that a conventional solution is unlikely to gain widespread adoption because of its requirement to install multiple fixed nodes (anchors) in each room. This paper therefore places its focus on (i) discussing radiolocalization methods that reduce the required infrastructure by exploiting information from reflected multipath components and (ii) showing that knowledge about the propagation environment enables localization with high accuracy and robustness. It is demonstrated that new millimeter-wave (mm-wave) technology, under investigation for 5G communications systems, will be able to provide cm-accuracy indoor localization in a robust manner, ideally suited for AL.

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Large amounts of methane (CH4) are known to be emitted from permafrost environments during the autumn freeze‐in, but the specific soil conditions leading up to these bursts are unclear. Therefore, we used an ultrawide band ground‐penetrating radar in Northeast Greenland in autumn 2009 to estimate the volumetric composition inside the soil through dielectric characterization from 200 to 3200 MHz. Our results suggest a compression of the gas reservoir during the phase transition of soil water, which is accompanied by a peak in surface CH4 emissions. About 1 week thereafter, there seems to be a decompression event, consistent with ground cracking which allows the gas reservoir to expand again. This coincides with the largest CH4 emission, exceeding the summer maximum by a factor of 4. We argue that these complementary measurement techniques are needed to come to an understanding of tundra CH4 bursts connected to soil freezing.

Abstract. The 60 GHz band is very promising for high data rate (>1 Gb/s) wireless systems operating at short ranges. However, due to the short wavelengths in this frequency band, the shadowing effects cuased by human bodies and furniture are severe and need to be modeled properly. In this paper, we present an experimental, measurement-based characterization of the reflection and shadowing effects in the 60 GHz band caused by human bodies and various phantoms, in order to find simple phantoms suitable for use in human shadowing measurements. It is shown that a waterfilled human phantom serves as a good choice for this purpose.

Pathloss is typically modeled using a log-distance power law with a large-scale fading term that is log-normal. However, the received signal is affected by the dynamic range and noise floor of the measurement system used to sound the channel, which can cause measurement samples to be truncated or censored. If the information about the censored samples are not included in the estimation method, as in ordinary least squares estimation, it can result in biased estimation of both the pathloss exponent and the large scale fading. This can be solved by applying a Tobit maximum-likelihood estimator, which provides consistent estimates for the pathloss parameters. This letter provides information about the Tobit maximum-likelihood estimator and its asymptotic variance under certain conditions.Comment: 4 pages, 3 figures. Published in IEEE Wireless Communication Letter

This paper generalizes a propagation graph model to polarized indoor wireless channels. In the original contribution, the channel is modelled as a propagation graph in which vertices represent transmitters, receivers and scatterers while edges represents the propagation conditions between vertices. Each edge is characterized by an edge transfer function accounting for the attenuation, delay spread and phase shift on the edge. In this contribution, we extend this modelling formalism to polarized channels by incorporating depolarization effects into the edge transfer functions and hence, the channel transfer matrix. We derive closed form expressions for the polarimetric power delay spectrum and cross-polarization ratio of the indoor channel. The expressions are derived considering average signal propagation in a graph and relate these statistics to model parameters, thereby providing a useful approach to investigate the averaged effect of these parameters on the channel statistics. Furthermore, we present a procedure for calibrating the model based on method of moments. Simulations were performed to validate the proposed model and the derived approximate expressions using both synthetic data and channel measurements at 15 GHz and 60 GHz. We observe that the model and approximate expressions provide good fits to the measurement data.

Abstract-Shadowing from vehicles can significantly degrade the performance of vehicle-to-vehicle (V2V) communication in multilink systems, e.g., vehicular ad-hoc networks (VANETs). It is thus important to characterize and model the influence of common shadowing objects like cars properly when designing these VANETs. Despite the fact that for multilink systems it is essential to model the joint effects on the different links, the multilink shadowing effects of V2V channels on VANET simulations are not yet well understood. In this paper we present a measurement based analysis of multilink shadowing effects in a V2V communication system with cars as blocking objects. In particular we analyze, characterize and model the large scale fading, both regarding the autocorrelation and the joint multilink cross-correlation process, for communication at 5.9 GHz between four cars in a highway convoy scenario. The results show that it is essential to separate the instantaneous propagation condition into line-of-sight (LOS) and obstructed LOS (OLOS), by other cars, and then apply an appropriate pathloss model for each of the two cases. The choice of the pathloss model not only influences the autocorrelation but also changes the cross-correlation of the large scale fading process between different links. By this, we conclude that it is important that VANET simulators should use geometry based models, that distinguish between LOS and OLOS communication. Otherwise, the VANET simulators need to consider the cross-correlation between different communication links to achieve results close to reality.

Vehicle-to-vehicle (V2V) wireless communications can improve traffic safety at road intersections and enable congestion avoidance. However, detailed knowledge about the wireless propagation channel is needed for the development and realistic assessment of V2V communication systems. We present a novel geometry-based stochastic MIMO channel model with support for frequencies in the band of 5.2-6.2 GHz. The model is based on extensive high-resolution measurements at different road intersections in the city of Berlin, Germany. We extend existing models, by including the effects of various obstructions, higher order interactions, and by introducing an angular gain function for the scatterers. Scatterer locations have been identified and mapped to measured multi-path trajectories using a measurement-based ray tracing method and a subsequent RANSAC algorithm. The developed model is parameterized, and using the measured propagation paths that have been mapped to scatterer locations, model parameters are estimated. The time variant power fading of individual multi-path components is found to be best modeled by a Gamma process with an exponential autocorrelation. The path coherence distance is estimated to be in the range of 0-2 m. The model is also validated against measurement data, showing that the developed model accurately captures the behavior of the measured channel gain, Doppler spread, and delay spread. This is also the case for intersections that have not been used when estimating model parameters.