Performance Evaluation of LTE Physical Layer

Hajlaoui, Emna; Khlifi, Abdelhakim; Zaier, Aida; Ghodhbane, Jihed; Hamed, Mouna Ben; Sbita, Lassaâd

doi:10.1109/iintec48298.2019.9112117

Cited by 2 publications

(1 citation statement)

References 5 publications

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“…In wireless communications, data rates have increased exponentially from second generation (2G/GSM) (data speed up to 9.6 Kbps) to 4G/LTE (data speed up to 300 Mbps). LTE employs 20 MHz bandwidth with different modulation and multiplexing techniques such as Orthogonal Frequency Division Multiple Access (OFDM), 64 QAM, and 4 × 4 spatial multiplexing to achieve higher data rates [2,3]. Higher-resolution video and audio content have become widespread with reliable and fast mobile internet access.…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning-Based Uplink Throughput Prediction from Physical Layer Measurements

2022

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The uplink (UL) throughput prediction is indispensable for a sustainable and reliable cellular network due to the enormous amounts of mobile data used by interconnecting devices, cloud services, and social media. Therefore, network service providers implement highly complex mobile network systems with a large number of parameters and feature add-ons. In addition to the increased complexity, old-fashioned methods have become insufficient for network management, requiring an autonomous calibration to minimize utilization of the system parameter and the processing time. Many machine learning algorithms utilize the Long-Term Evolution (LTE) parameters for channel throughput prediction, mainly in favor of downlink (DL). However, these algorithms have not achieved the desired results because UL traffic prediction has become more critical due to the channel asymmetry in favor of DL throughput closing rapidly. The environment (urban, suburban, rural areas) affect should also be taken into account to improve the accuracy of the machine learning algorithm. Thus, in this research, we propose a machine learning-based UL data rate prediction solution by comparing several machine learning algorithms for three locations (Houston, Texas, Melbourne, Florida, and Batman, Turkey) and determine the best accuracy among all. We first performed an extensive LTE data collection in proposed locations and determined the LTE lower layer parameters correlated with UL throughput. The selected LTE parameters, which are highly correlated with UL throughput (RSRP, RSRQ, and SNR), are trained in five different learning algorithms for estimating UL data rates. The results show that decision tree and k-nearest neighbor algorithms outperform the other algorithms at throughput estimation. The prediction accuracy with the R2 determination coefficient of 92%, 85%, and 69% is obtained from Melbourne, Florida, Batman, Turkey, and Houston, Texas, respectively.

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Section: Introductionmentioning

confidence: 99%