Die neue Universität Frankfurt

Die Universität Frankfurt steht vor der größten Herausforderung in ihrer annähernd 90-jährigen Geschichte. Unterstützt und getragen durch den Willen der politischen Entscheider beim Land und der Stadt wird die Universität ihren Gründungsstandort in Bockenheim schrittweise aufgeben. ..

A Deep Reinforcement Learning-based Approach for Adaptive Handover Protocols in Mobile Networks

Due to an ever-increasing number of participants and new areas of application, the demands on mobile communications systems are continually increasing. In order to deliver higher data rates, enable mobility and guarantee QoS requirements of subscribers, these systems and the protocols used are becoming more complex. By using higher frequency spectrums, cells become smaller and more base stations have to be deployed. This leads to an increased number of handovers of user equipments between base stations in order to enable mobility, resulting in potentially more frequent radio link failures and rate reduction. The persistent switching between the same base stations, commonly referred to as "ping-pong", leads to a consistent reduction of data rates. In this work, we propose a method for handover optimization by using proximal policy optimization in mobile communications to learn an adaptive handover protocol. The resulting agent is highly flexible regarding different travelling speeds of user equipments, while outperforming the standard 5G NR handover protocol by 3GPP in terms of average data rate and number of radio link failures. Furthermore, the design of the proposed environment demonstrates remarkable accuracy, ensuring a fair comparison with the standard 3GPP protocol.Comment: Submitted to EuCN

Resource Allocation with Stability Constraints of an Edge-cloud controlled AGV

The paper proposes Resource Allocation (RA) schemes for a closed loop feedback control system by analysing the control-communication dependencies. We consider an Automated Guided Vehicle (AGV) that communicates with a controller located in an edge-cloud over a wireless fading channel. The control commands are transmitted to an AGV and the position state is feedback to the controller at every time-instant. A control stability based scheduling metric 'Probability of Instability' is evaluated for the resource allocation. The performance of stability based RA scheme is compared with the maximum SNR based RA scheme and control error first approach in an overloaded and non-overloaded scenario. The RA scheme with the stability constraints significantly reduces the resource utilization and is able to schedule more number of AGVs while maintaining its stability. Moreover, the proposed RA scheme is independent of control state and depends upon consecutive packet errors, the control parameters like sampling time and AGV velocity. Furthermore, we also analyse the impact of RA schemes on the AGV's stability and error performance, and evaluated the number of unstable AGVs