AN AUTOMATED MACHINE LEARNING BASED ANALYSIS OF PROPAGATED RADIO SIGNAL PARAMETERS AND CONDITIONS IN OPERATIONAL BROADBAND CELLULAR NETWORK
DOI:
https://doi.org/10.60787/jnamp.vol69no2.530Keywords:
Network monitoring, coverage quality, RSRP, SNR, Machine learning, Fitrauto algorithmAbstract
Radio signal coverage and quality shrinkage are two key factors that place critical limitations on good data transmission quality provisioning over mobile broadband networks channels. Owing to numerous multipath received signal fading conditions, eNodeBs transmitter deterioration and general frequent changes in radio propagation settings. Factors considered, include intracellular interference problems and stochastic background noise around the environment of the deployed operational networks. In contribution, practical monitoring and status service quality correlation analysis of deployed operational commercial LTE networks, considering signal coverage and quality indicators such as received signal power (RSRP), received signal strength indicator (RSSI), and, Signal-to-Noise ratio (SNR) is presented for a typical urban terrain. The aim realized by conducting a detailed field test measurement using TEMS investigation equipment. Post data analysis of collected LTE system network parameters provided, results and findings also could provide basis for effective design and performance optimisation of different mobile broadband networks.
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