scholarly journals Performance Comparison of ANN Training Algorithms for Hysteresis Determination in LTE networks

2019 ◽  
Vol 1378 ◽  
pp. 042094
Author(s):  
E E Ekong ◽  
A A Adewale ◽  
A Ben-Obaje ◽  
A M Alalade ◽  
C N Ndujiuba
2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Nida Nurvira ◽  
Anggun Fitrian Isnawati ◽  
Achmad Rizal Danisya

Increasing user requirements for LTE networks, data traffic from eNodeB to core network is also increases, therefore, the recommended solution for meeting this high data traffic is to use a backhaul network design. Backhaul is the path or network used to connect eNodeB with the core network. In this research, backhaul technology used is wi-fi 802.11ac backhaul and microwave backhaul. In this study begins by collecting existing data, then perform capacity calculations to find out the number of eNodeB needed and to find out the capacity of the backhaul links to be designed, then determine the antenna height to achieve LOS conditions, then calculate the desired performance standards and calculate the backhaul network link budget on microwave and wi-fi technologies. Based on the calculation results in terms of capacity, the total user target is 90,167 users and has a throughput capacity per eNodeB of 61 Mbps. In the link-capacity calculation, the total link capacity is 427 Mbps. From the simulation results that using microwave technology, the average RSL value is -30.90 dBm, the value meets the -57 dBm threshold standard and the value of availability does not meet the standard of 99.999% because the average value obtained is 99.998095%. Whereas for wi-fi technology, the average RSL value is -39.24 dBm and meet the -72 dBm threshold standard, for the average availability value meets 99.999% standard, with a value of 100%. From the results of the two technologies, can be conclude that the wi-fi technology is more suitable for the use of backhaul network design in Ciputat Sub-district.


2006 ◽  
Vol 327 (1-2) ◽  
pp. 126-138 ◽  
Author(s):  
A. Ghaffari ◽  
H. Abdollahi ◽  
M.R. Khoshayand ◽  
I. Soltani Bozchalooi ◽  
A. Dadgar ◽  
...  

2019 ◽  
Vol 36 (6) ◽  
pp. 1820-1834 ◽  
Author(s):  
Sree Ranjini K.S.

Purpose In recent years, the application of metaheuristics in training neural network models has gained significance due to the drawbacks of deterministic algorithms. This paper aims to propose the use of a recently developed “memory based hybrid dragonfly algorithm” (MHDA) for training multi-layer perceptron (MLP) model by finding the optimal set of weight and biases. Design/methodology/approach The efficiency of MHDA in training MLPs is evaluated by applying it to classification and approximation benchmark data sets. Performance comparison between MHDA and other training algorithms is carried out and the significance of results is proved by statistical methods. The computational complexity of MHDA trained MLP is estimated. Findings Simulation result shows that MHDA can effectively find the near optimum set of weight and biases at a higher convergence rate when compared to other training algorithms. Originality/value This paper presents MHDA as an alternative optimization algorithm for training MLP. MHDA can effectively optimize set of weight and biases and can be a potential trainer for MLPs.


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