Dimensionality Reduction for Optimization of Radio Base Station Transmission Based on Energy Efficiency
Information
Large amounts of data are available to determine the current energy efficiency. The more data that needs to be processed to find the necessary result the greater the costs incurred in terms of time and processing power etc. This data must be processed frugally so that we are not using more processing power and data storage than needed to initially identify anomalies in the energy efficiency and so that this can be done in the minimum amount of time.
The purpose of the technique described in this paper is to address the 'curse of dimensionality' whereby high dimensional data "may cause the deterioration of many fault detection techniques because the degree of data
abnormality in fault-relevant dimensions can be obscured or even masked by irrelevant attributes" [2]. It involves reducing the dimensions necessary to quickly and efficiently identify entities in the radio network with anomalous energy efficiency so that they can be further analysed to identify the cause of the inefficiencies and to decide on the relevant remedial action in terms of
reconfiguration in each case. This technique could be used to generate a list of network entities highlighted for further human intervention or it could be used to generate input to a subsequent function which would examine each case in more detail for the purpose of making specific configuration changes autonomically to resolve the energy efficiency issues identified by this technique.
DOI: 10.1109/ITMS59786.2023.10317744
Author/Speaker/Contributor
Joss Armstrong (LMI), Enda Fallon,
Event/Publication
2023 IEEE 64th International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS)
Date
October 5, 2023
