Abstract: The current and future cellular mobile communication networks generate enormous amounts of data. Networks have become extremely complex with extensive space of parameters, features and counters. These networks are unmanageable with legacy methods and an enhanced design and optimization approach is necessary that is increasingly reliant on machine learning. This paper proposes that machine learning as a viable approach for uplink throughput prediction. LTE radio metric, such as Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), and Signal to Noise Ratio (SNR) are used to train models to estimate expected uplink throughput. The prediction accuracy with high determination coefficient of 91.2% is obtained from measurements collected with a simple smartphone application.
Abstract: The main aim of this paper is to dedicate the belt drive system faults like cogs missing, misalignment and belt worm using vibration analysis technique. Experimentally, the belt drive test-rig is equipped to measure vibrations signals under different operating conditions. Finite element 3D model of belt drive system is created and vibration response analyzed using commercial finite element software ABAQUS/CAE. Root mean square (RMS) and Crest Factor will serve as indicators of average amplitude of envelope analysis signals. The vibration signals pattern obtained from the simulation model and experimental data have the same characteristics. It can be concluded that each case of the RMS is more effective in detecting the defect for acceleration response. While Crest Factor parameter has a response with the displacement and velocity of vibration signals. Also it can be noticed that the model has difficulty in completing the solution when the misalignment angle is higher than 1 degree.
Abstract: This paper explains the cause of nonlinearity in floor
attenuation hither to left unexplained. The performance degradation
occurring in air interface for GSM signals is quantitatively analysed
using the concept of Radiating Columns of buildings. The signal
levels were measured using Wireless Network Optimising Drive Test
Tool (E6474A of Agilent Technologies). The measurements were
taken in reflected signal environment under usual fading conditions
on actual GSM signals radiated from base stations. A mathematical
model is derived from the measurements to predict the GSM signal
levels in different floors. It was applied on three buildings and found
that the predicted signal levels deviated from the measured levels
with in +/- 2 dB for all floors. It is more accurate than the prediction
models based on Floor Attenuation Factor. It can be used for
planning proper indoor coverage in multi storey buildings.