The introduction of wireless networks for mobile communication made their optimization a central issue due to the ever-increasing demand. With current wireless networks such as 5G, the key parameters are not only the data rate and capacity, but also requirements regarding energy efficiency, latency, eavesdropping security and others. Thus, enormous amounts of data can accumulate which can only be advantageously processed by means of Machine Learning.
The mobile terminals collect the major part of the data. Therefore, they use their transmission and additionally recorded environmental data to gain a more comprehensive overview of the mobile communication cell supplying them. Using this cognitively acquired additional knowledge, it becomes possible to act with some foresight. Further information, e.g. from the Internet, can be used to support this. For example, the automatic evaluation of an event calendar can provide the location and time of major events, which can then be taken into account in advance when optimizing a wireless network.
The long-term goal is not only to enable the individual mobile user to gain cognitive abilities with regard its immediate environment, but also to combine and further process the cognitive knowledge of all mobile users in such a way that cognitive mobile communications management is achieved, i.e. that the wireless network can control itself largely autonomously.
In order to achieve this goal, the department Wireless Communications and Networks develops, investigates and tests various methods for the optimization of wireless networks. One goal is, for example, to produce detailed maps of the entire wireless network for various relevant parameters, which can then be evaluated to achieve the best possible condition for the wireless network. Relevant parameters are, for example, the traffic density or the capacity of a network, whose relationships, including the input data used for this purpose, are shown as examples in the following figure.
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