Machine Learning for Beam Based Mobility Optimization in NR

• Main Author: Ekman, Björn
• Format: Others
• Language: English
• Published: Linköpings universitet, Kommunikationssystem 2017
• Subjects: machine learning; 5G; random forest; multi-class classification; multi-target regression; Communication Systems; Kommunikationssystem
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-136489

One option for enabling mobility between 5G nodes is to use a set of area-fixed reference beams in the downlink direction from each node. To save power these reference beams should be turned on only on demand, i.e. only if a mobile needs it. An User Equipment (UE) moving out of a beam's coverage will require a switch from one beam to another, preferably without having to turn on all possible beams to find out which one is the best. This thesis investigates how to transform the beam selection problem into a format suitable for machine learning and how good such solutions are compared to baseline models. The baseline models considered were beam overlap and average Reference Signal Received Power (RSRP), both building beam-to-beam maps. Emphasis in the thesis was on handovers between nodes and finding the beam with the highest RSRP. Beam-hit-rate and RSRP-difference (selected minus best) were key performance indicators and were compared for different numbers of activated beams. The problem was modeled as a Multiple Output Regression (MOR) problem and as a Multi-Class Classification (MCC) problem. Both problems are possible to solve with the random forest model, which was the learning model of choice during this work. An Ericsson simulator was used to simulate and collect data from a seven-site scenario with 40 UEs. Primary features available were the current serving beam index and its RSRP. Additional features, like position and distance, were suggested, though many ended up being limited either by the simulated scenario or by the cost of acquiring the feature in a real-world scenario. Using primary features only, learned models' performance were equal to or worse than the baseline models' performance. Adding distance improved the performance considerably, beating the baseline models, but still leaving room for more improvements.