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The 6G mobile research lab is focused on key aspects and challenges related to future mobile networks and emerging wireless technologies. Our research is oriented mainly on topics related to control and management layers. This covers mobility and radio resource management for self-optimizing and energy efficient networks. Furthermore, we address network architecture including Multi-Access Edge Computing (MEC), Cloud-Radio Access Network (C-RAN) and aerial nodes, such as drones, UAVs, HAPs, or satellites. We also tackle challenges in the area of multimodal semantic communications and computing. From a perspective of scenarios, the 6Gm research is oriented mainly on device-to-device (D2D) communication, vehicular communications, non-terrestrial networks, robotics, IoT, and heterogeneous networks. Our research covers theoretical aspects exploiting various optimization techniques, game theory, and machine learning/artificial intelligence. We also deliver practical verification of our ideas in the laboratory equipped with software defined mobile networks.

Overview of research topics

Main Research areas

For cellular networks, mobility is an essential feature. Mobility management procedures at all stages, i.e., scanning of neighborhood, handover decision and call admission control, for future mobile networks cope especially with high density of base stations. The management enabling users’ mobility must guarantee quality required by users in quickly changing environment with heterogeneous services, technologies and cells. Efficient management of such a complex problem can be ensured by self-organizing and optimizing algorithms exploiting prediction and machine learning approaches to estimate future characteristics and behavior of users and network and adapt parameters of mobility management accordingly.

Neighborhood scanning

We design algorithms optimizing procedure of scanning of potential target cells if a user is moving. Our first approach is based on dynamic optimization of a set of scanned cells according to the SINR observed by a user equipment from its serving cell. If the user equipment is in the cell center, only the serving cell is scanned. Contrary, if the UE moves closer to the cell edge, also other cells are inserted to the list of scanned cells. The cells are included in the list based on the probability of handover to these cells. Furthermore, we work on new scanning scheme maximizing utilization of the small cells and minimizing energy consumption due to scanning. The proposal exploits graph theory to represent a principle of obstructed paths in combination with knowledge of previous visited cell and estimated distance between cells.


Handover decision

For handover decision, we improve efficiency of handover decision process to avoid redundant handovers between neighboring cells. We targets mainly scenarios with densely deployed small cells as these are an integral part of 5G mobile networks. Our approaches include adaptive hysteresis or estimation of gain experienced by users if he/she performs handover to a target cell.


Fast Cell Selection

We have also proposed coordinated communication of a user with several neighboring cells by means of Fast Cell Selection in OFDMA networks. For each frame, the most suitable cell which transmits/receive data to/from users is selected. This approach enables to minimize the number of hard handovers and, consequently, the interruption in communication due to handover is eliminated.


Call Admission Control

For Call admission control, we define algorithms predicting signal quality received by the user just after handover based on knowledge of current signal levels observed from serving and neighboring cells. Together with prediction of user mobility and handover, we can efficiency decide about admission of users to a target cell and reduce number of call drops.