5G has emerged to revolutionize the telecommunications industry. With its many possibilities, there are also great challenges, such as maintaining the increased complexity of themany parameters in these new networks. It is a common practice to test new features of thenetworks before employing them, and this is often done in a simulated environment. Thetask of this thesis was to investigate if self-adaptive software, in simulations at Ericsson,could dynamically change the bandwidth to increase the net throughput while minimizingthe packet loss, i.e. to maximize the overall quality of service on the network, without theneed of human intervention. A simple simulation of a 5G network was created to trainand test the effect of two proposed AI-models. The models tested were Proximal PolicyOptimization and Deep Deterministic Policy Gradient, where the former model showedpromising results while the latter did not yield any significant improvements comparedto the benchmarks. The study indicates that self-adaptive software, in simulated environments, can effectively be achieved by using AI while increasing the quality of service.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-204562 |
| Date | January 2024 |
| Creators | Jönsson, Axel, Hammarhjelm, Erik |
| Publisher | Linköpings universitet, Institutionen för datavetenskap |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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