Due to its flexibility, cost-efficiency, and the ability to support mobility, wireless connectivity is seen today as a key enabler for a wide range of applications beyond classical mobile communications. A significant part of these applications depends on the capability of the wireless communication system to provide reliable connectivity. However, due to the randomness of the wireless propagation channel, reliability is still a critical issue in these systems. Some applications, such as vehicular and industrial applications, demand a level of reliability that wireless communication systems typically are not able to guarantee. This paper provides a framework that enables these applications to make use of wireless connectivity only if the transmission conditions are favorable enough. The concept is based on the idea that -despite the fact that it is practically impossible to ensure error-free wireless communication -it is feasible to derive boundary conditions for the transmission success. To this end, the paper introduces a novel metric for UltraReliable Communication (URC) referred to as "Availability", that determines the expected presence or absence of link reliability at the time of transmission. The availability is signaled by means of an Availability Indicator (AI) to the applications. Moreover, we develop the system model for computing the AI and illustrate the potential benefits of the new reliability metric by means of a possible implementation for automotive scenarios.

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Device-to-Device (D2D) communications underlaying cellular network exploits physical proximity of the devices and aims at enhancing resource utilization, coverage, data rates and Quality of Service (QOS). Further, it enables the network operators to offload conventional network routed traffic to direct peer-to-peer links and could also support new application fields, such as Machine-to-Machine (M2M) and Vehicle-to-Vehicle (V2V), communication. In order to enable D2D communication, allocating resources (Physical resource blocks: PRBs) to D2D links by reusing cellular resources is a vital procedure. There are several resource allocation (RA) schemes that facilitate resource reuse of cellular PRBs for D2D communication. In most of these schemes, the crucial information for deciding upon PRB reuse is positions of D2D pairs and cellular users, so that mutual interference can be reduced. Further, there are certain schemes that rely on angular information of users to carry out resource allocation for D2D users. Thus, accuracy of position information plays an important role for employing these RA schemes in real world. This paper discusses RA schemes that are based on position/angular information and evaluates the robustness of location based RA schemes against positioning errors in real world deployment.Index Terms-D2D communication, location information based resource allocation, virtual sectoring, position estimation

Short Range wireless devices are becoming more and more popular for ubiquitous sensor and actuator connectivity in industrial communication scenarios. Apart from communicationonly scenarios, there are also mission-critical use cases where the distance between the two communicating nodes needs to be determined precisely. Applications such as Automatic Guided Vehicles (AGV's), Automatic Train Pairing additionally require the devices to scan the environment and detect any potential humans/obstacles. Ultra-Wide Band (UWB) has emerged as a promising candidate for Real-Time Ranging and Localization (RTRL) due to advantages such as large channel capacity, better co-existence with legacy systems due to low transmit power, better performance in multipath environments etc. In this paper, we evaluate the performance of a UWB COTS device -TimeDomain P440 which can operate as a ranging radio and a monostatic radar simultaneously. To this end, we evaluate the possibility of using Supervised Learning based estimators for predicting the presence of obstacles by constructing a multiclass hypothesis. Simulation results show that the Ensemble tree based methods are able to calculate the likelihood of obstacle collision with accuracies close to 95%.