US 12,170,908 B2
Detecting interference in a wireless network
Chin Lam Eng, Tokyo (JP); Philipp Frank, Madrid (ES); Raul Martin Cuerdo, Madrid (ES); Mitchell Ho, Sydney (AU); and Chee Wai Ng, Sydney (AU)
Assigned to TELEFONAKTIEBOLAGET LM ERICSSON (PUBL), Stockholm (SE)
Appl. No. 17/430,207
Filed by Telefonaktiebolaget LM Ericsson (publ), Stockholm (SE)
PCT Filed Feb. 15, 2019, PCT No. PCT/EP2019/053856
§ 371(c)(1), (2) Date Aug. 11, 2021,
PCT Pub. No. WO2020/164739, PCT Pub. Date Aug. 20, 2020.
Prior Publication US 2022/0167183 A1, May 26, 2022
Int. Cl. H04W 24/02 (2009.01); G06N 3/045 (2023.01); G06N 3/08 (2023.01); H04W 24/10 (2009.01)
CPC H04W 24/02 (2013.01) [G06N 3/045 (2023.01); G06N 3/08 (2013.01); H04W 24/10 (2013.01)] 16 Claims
OG exemplary drawing
 
1. A method for generating and training a model to detect interference conditions at a cell in a wireless cellular network and to classify the impact of detected interference conditions on performance of the wireless cellular network in the cell, the method comprising:
for each of a plurality of cells in the wireless cellular network:
obtaining data representing received signal power at a base station serving the cell over a period of time;
obtaining a classification of the received signal power data into one of a plurality of cell interference conditions;
obtaining data representing a plurality of performance metrics for the cell over the time period; and
obtaining a classification of the performance metric data into one of a plurality of cell impact classes;
the method further comprising:
applying a Multi-Task Learning (MTL) Machine Learning (ML) algorithm to a training data set comprising the classified received signal power and performance metric data to generate a model for classifying received signal power data into one of the plurality of cell interference conditions and for classifying performance metric data into one of the plurality of cell impact classes, wherein applying an MTL ML algorithm to a training data set comprising the classified received signal power and performance metric data comprises:
learning in parallel a feature representation for each task from the set of tasks comprising classifying received signal power data into one of the plurality of cell interference conditions and classifying performance metric data into one of the plurality of cell impact classes;
combining the feature representations learned for each of the tasks; and
jointly learning a shared feature representation for both tasks and parameters for the shared model to perform both tasks.