Wireless Interference Identification with Convolutional Neural Networks

The steadily growing use of license-free frequency bands requires reliable coexistence management for deterministic medium utilization. For interference mitigation, proper wireless interference identification (WII) is essential. In this work we propose the first WII approach based upon deep convolutional neural networks (CNNs). The CNN naively learns its features through self-optimization during an extensive data-driven GPU-based training process. We propose a CNN example which is based upon sensing snapshots with a limited duration of 12.8 {\mu}s and an acquisition bandwidth of 10 MHz. The CNN differs between 15 classes. They represent packet transmissions of IEEE 802.11 b/g, IEEE 802.15.4 and IEEE 802.15.1 with overlapping frequency channels within the 2.4 GHz ISM band. We show that the CNN outperforms state-of-the-art WII approaches and has a classification accuracy greater than 95% for signal-to-noise ratio of at least -5 dB

A Software-Defined Channel Sounder for Industrial Environments with Fast Time Variance

Novel industrial wireless applications require wideband, real-time channel characterization due to complex multipath propagation. Rapid machine motion leads to fast time variance of the channel's reflective behavior, which must be captured for radio channel characterization. Additionally, inhomogeneous radio channels demand highly flexible measurements. Existing approaches for radio channel measurements either lack flexibility or wide-band, real-time performance with fast time variance. In this paper, we propose a correlative channel sounding approach utilizing a software-defined architecture. The approach enables real-time, wide-band measurements with fast time variance immune to active interference. The desired performance is validated with a demanding industrial application example.Comment: Submitted to the 15th International Symposium on Wireless Communication Systems (ISWCS 2018

Cluster-Building and the Transformation of the University

Eine der bemerkenswerten neueren Veränderungen von Universitäten in vielen westlichen Ländern besteht in Bemühungen, Forschungscluster, »kritische Massen«, Zentren etc. zu errichten. In diesem Beitrag wollen wir die enge Verbindung zwischen Cluster-Bildung auf der einen Seite und zwei weiteren neueren Transformationen des Hochschulsystems untersuchen: Die entstehende Actorhood, die sich nicht zuletzt in der Stärkung von Hochschulleitungen manifestiert, und die an letztere adressierte Erwartung, Profilbildung an ihren Universitäten voranzutreiben. Wir beginnen mit einer Beschreibung dessen, was wir unter Clustern verstehen. Anschließend fragen wir im Hinblick auf Forscherinnen und Forscher einerseits und Hochschulleitungen andererseits, warum sich einige von den ersteren und die letzteren für Cluster-Bildung interessieren, während andere der erstgenannten Gruppe sie ablehnen. Danach betrachten wir das Zusammenspiel von top-down und bottom-up-Aktivitäten. Hier unterscheiden wir zwischen der Errichtung neuer Cluster und dem Umgang mit bestehenden. So folgen wir dem Lebenszyklus eines Clusters vom Anfang bis zum Ende.   One of the noticeable recent changes of universities in many Western countries consists in efforts to establish research clusters, »critical masses«, centers etc. In this paper we want to explore the tight connection between cluster-building, on the one hand, and two other recent transformations of the university system: the emerging actorhood of universities which manifests itself mainly in the strengthening of university leadership, and the expectation directed at university leadership that it should promote profile-building of its university. We start with a descriptive exposition of what is meant by clusters. Then we ask with respect to researchers, on the one hand, and university leadership, on the other, why the latter and some of the former have got interested in cluster-building whereas others of the former oppose. After that we take a look at the interplay of top-down and bottom-up activities involved in cluster-building. Here we distinguish the creation of a new cluster from the handling of an existing one. In this way we follow the life-cycle of a cluster from its beginning to its end

Resource Allocation for a Wireless Coexistence Management System Based on Reinforcement Learning

In industrial environments, an increasing amount of wireless devices are used, which utilize license-free bands. As a consequence of these mutual interferences of wireless systems might decrease the state of coexistence. Therefore, a central coexistence management system is needed, which allocates conflict-free resources to wireless systems. To ensure a conflict-free resource utilization, it is useful to predict the prospective medium utilization before resources are allocated. This paper presents a self-learning concept, which is based on reinforcement learning. A simulative evaluation of reinforcement learning agents based on neural networks, called deep Q-networks and double deep Q-networks, was realized for exemplary and practically relevant coexistence scenarios. The evaluation of the double deep Q-network showed that a prediction accuracy of at least 98 % can be reached in all investigated scenarios.Comment: Submitted to the 23rd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2018

A concept for anisotropic PTV margins including rotational setup uncertainties and its impact on the tumor control probability in canine brain tumors

Objective. In this modelling study, we pursued two main goals. The first was to establish a new CTV-to-PTV expansion which considers the closest and most critical organ at risk (OAR). The second goal was to investigate the impact of the planning target volume (PTV) margin size on the tumor control probability (TCP) and its dependence on the geometrical setup uncertainties. The aim was to achieve a smaller margin expansion close to the OAR while allowing a moderately larger expansion in less critical areas further away from the OAR and whilst maintaining the TCP. Approach. Imaging data of radiation therapy plans from pet dogs which had undergone radiation therapy for brain tumor were used to estimate the clinic specific rotational setup uncertainties. A Monte-Carlo methodology using a voxel-based TCP model was used to quantify the implications of rotational setup uncertainties on the TCP. A combination of algorithms was utilized to establish a computational CTV-to-PTV expansion method based on probability density. This was achieved by choosing a center of rotation close to an OAR. All required software modules were developed and integrated into a software package that directly interacts with the Varian Eclipse treatment planning system. Main results. Several uniform and non-isotropic PTVs were created. To ensure comparability and consistency, standardized RT plans with equal optimization constraints were defined, automatically applied and calculated on these targets. The resulting TCPs were then computed, evaluated and compared. Significance. The non-isotropic margins were found to result in larger TCPs with smaller margin excess volume. Further, we presented an additional application of the newly established CTV-to-PTV expansion method for radiation therapy of the spinal axis of human patients