Predicting the Spectral and Energy Efficiency of LTE Network

doi:10.23940/ijpe.24.10.p6.640647

Abstract

Abstract:

As the number of subscribers for Long Term Evolution (LTE) technology and associated resources increase, it has become mandatory for the LTE mobile operators to evaluate how efficiently the existing LTE cell network will perform in real-time scenarios. Controlling the transmitting services in the LTE cell network is an important factor as it assists in increasing the entire network execution. The best way to attain this is to attain the integrity between the users or achieve the efficient values of spectral efficiency (SE) and energy efficiency (EE). Spectral efficiency and energy efficiency are important parameters for a cellular network to exhibit its network performance level. In this paper, the real-time information records of approximately 50000 BTS sites, which were comprised of Key Performance Indicators (KPIs) such as Radio Resource Control (RRC), E-UTRAN Radio Access Bearer (ERAB), Packet drop, etc. have been considered. From the data set, throughput has been predicted using the Random Forest Algorithm. Further Spectral and Energy efficiency has been predicted. Spectral efficiency (SE) helps in controlling the transmitting power of the cell network and Energy Efficiency helps in enhancing the network quality of service (QoS). The maximum value of the spectral and energy efficiency at the uplink throughput and downlink throughput had been taken from the Nokia Drive Test sheet. If the predicted values lie beyond the limits of the network, it will indicate that the network performance has deteriorated and has scope of improvement. The resulting variables were chosen on the basis of the real field assessment by the Nokia Network Pvt. Ltd in India.

Key words: energy efficiency, key performance indicator, long term evolution, machine learning (ML), spectral efficiency (SE)

Hak Gupta Sindhu, Tyagi Abhishek, and Sharma Richa. Predicting the Spectral and Energy Efficiency of LTE Network [J]. Int J Performability Eng, 2024, 20(10): 640-647.

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