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http://dx.doi.org/10.11003/JPNT.2019.8.4.193

Neural Networks Based Modeling with Adaptive Selection of Hidden Layer's Node for Path Loss Model  

Kang, Chang Ho (Department of Mechanical System Engineering, Kumoh National Institute of Technology)
Cho, Seong Yun (Department of Robotics Engineering, Kyungil University)
Publication Information
Journal of Positioning, Navigation, and Timing / v.8, no.4, 2019 , pp. 193-200 More about this Journal
Abstract
The auto-encoder network which is a good candidate to handle the modeling of the signal strength attenuation is designed for denoising and compensating the distortion of the received data. It provides a non-linear mapping function by iteratively learning the encoder and the decoder. The encoder is the non-linear mapping function, and the decoder demands accurate data reconstruction from the representation generated by the encoder. In addition, the adaptive network width which supports the automatic generation of new hidden nodes and pruning of inconsequential nodes is also implemented in the proposed algorithm for increasing the efficiency of the algorithm. Simulation results show that the proposed method can improve the neural network training surface to achieve the highest possible accuracy of the signal modeling compared with the conventional modeling method.
Keywords
auto-encoder network; adaptive selection of hidden layer's node; LTE path loss model; signal strength attenuation;
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1 Deng, L., Seltzer, M. L., Yu, D., Acero, A., Mohamed, A. R., et al. 2010, Binary coding of speech spectrograms using a deep auto-encoder, Proc. Interspeech, pp.1692-1695.
2 Gogna, A., Majumdar, A., & Ward, R. 2016, Semi-supervised stacked label consistent autoencoder for reconstruction and analysis of biomedical signals, IEEE Transactions on Biomedical Engineering, 64, 2196-2205. https://doi.org/10.1109/TBME.2016.2631620   DOI
3 Lu, X., Tsao, Y., Matsuda, S., & Hori, C. 2013, Speech enhancement based on deep denoising autoencoder, in Proc. Interspeech, pp.436-440.
4 Mom, J. M., Mgbe, C. O., & Igwue, G. A. 2014, Application of artificial neural network for path loss prediction in urban macrocellular environment, American Journal of Engineering Research, 3, 270-275.
5 Ostlin, E., Zepernick, H. J., & Suzuki, H. 2010, Macrocell path-loss prediction using artificial neural networks, IEEE Transactions on Vehicular Technology, 59, 2735-2747. https://doi.org/10.1109/TVT.2010.2050502   DOI
6 Gondara, L. 2016, Medical image denoising using convolutional denoising autoencoders, Proc. IEEE 16th Int. Conf. Data Mining Workshops (ICDMW), 12-15 Dec. 2016, Barcelona, Spain, pp.241-246. https://doi.org/10.1109/ICDMW.2016.0041
7 Hamid, M., & Kostanic, I. 2013, Path loss models for LTE and LTE-A relay stations, Universal journal of communications and network, 1, 119-126. https://doi.org/10.13189/ujcn.2013.010401   DOI
8 Hosseinzadeh, S., Almoathen, M., Larijani, H., & Curtis, K. 2017, A Neural Network Propagation Model for LoRaWAN and Critical Analysis with Real-World Measurements, Big Data and Cognitive Computing, 1, 7. https://doi.org/10.3390/bdcc1010007   DOI
9 Lai, Y. H., Chen, F., Wang, S. S., Lu, X., Tsao, Y., et al. 2016, A deep denoising autoencoder approach to improving the intelligibility of vocoded speech in cochlear implant simulation, IEEE Transactions on Biomedical Engineering, 64, 1568-1578. https://doi.org/10.1109/TBME.2016.2613960   DOI
10 Liu, G. Y., Chang, T. Y., Chiang, Y. C., Lin, P. C., & Mar, J. 2017, Path Loss Measurements of Indoor LTE System for the Internet of Things, Applied Sciences, 7, 537. https://doi.org/10.3390/app7060537   DOI
11 Liu, P., Zheng, P., & Chen, Z. 2019, Deep Learning with Stacked Denoising Auto-Encoder for Short-Term Electric Load Forecasting, Energies, 12, 2445. https://doi.org/10.3390/en12122445   DOI
12 Park, C., Tettey, D. K., & Jo, H. S. 2019, Artificial Neural Network Modeling for Path Loss Prediction in Urban Environments. https://arxiv.org/abs/1904.02383
13 Popescu, I., Nikitopoulos, D., Constantinou, P., & Nafornita, I. 2006, ANN Prediction Models for Outdoor Environment, 2006 IEEE 17th International Symposium on Personal, Indoor and Mobile Radio Communications, 11-14 Sept. 2006, Helsinki, Finland, pp.1-5. https://doi.org/10.1109/PIMRC.2006.254270
14 Pratama, M., Ashfahani, A., Ong, Y. S., Ramasamy, S., & Lughofer, E.2018a, Autonomous Deep Learning: Incremental Learning of Denoising Autoencoder for Evolving Data Streams. https://arxiv.org/abs/1809.09081
15 Pratama, M., Dimla, E., Tjahjowidodo, T., Pedrycz, W., & Lughofer, E. 2018b, Online tool condition monitoring based on parsimonious ensemble+, IEEE transactions on cybernetics, early access. https://doi.org/10.1109/TCYB.2018.2871120
16 Pratama, M., Pedrycz, W., & Lughofer, E. 2018c, Evolving ensemble fuzzy classifier, IEEE Transactions on Fuzzy Systems, 26, 2552-2567. https://doi.org/10.1109/TFUZZ.2018.2796099   DOI
17 Vincent, P., Larochelle, H., Bengio, Y., & Manzagol, P. A. 2008, Extracting and composing robust features with denoising autoencoders, Proc. 25th Int. Conf. Mach. Learn., pp.1096-1103. https://doi.org/10.1145/1390156.1390294
18 Pratama, M., Pedrycz, W., & Webb, G. I. 2018d, An incremental construction of deep neuro fuzzy system for continual learning of non-stationary data streams. https://arxiv.org/abs/1808.08517
19 Rusu, A. A., Rabinowitz, N. C., Desjardins, G., Soyer, H., Kirkpatrick, J., et al. 2016, Progressive neural networks. https://arxiv.org/abs/1606.04671
20 Song, C., Liu, F., Huang, Y., Wang, L., & Tan, T. 2013, Auto-encoder based data clustering, Iberoamerican Congress on Pattern Recognition (Berlin: Springer), pp.117-124. https://doi.org/10.1007/978-3-642-41822-8_15
21 Yoon, J., Yang, E., Lee, J., & Hwang, S. J. 2017, Lifelong learning with dynamically expandable networks. https://arxiv.org/abs/1708.01547
22 Zhou, G., Sohn, K., & Lee, H. 2012, Online incremental feature learning with denoising autoencoders, In Artificial intelligence and statistics, La Palma, Canary Islands, pp.1453-1461.
23 Zyoud, A., Habaebi, M. H., & Islam, R. 2016, Parameterized indoor propagation model for mobile communication links, Microwave and Optical Technology Letters, 58, 823-826. https://doi.org/10.1002/mop.29671   DOI
24 Cheerla, S., Ratnam, D. V., & Borra, H. S. 2018, Neural networkbased path loss model for cellular mobile networks at 800 and 1800 MHz bands, AEU-International Journal of Electronics and Communications, 94, 179-186. https://doi.org/10.1016/j.aeue.2018.07.007   DOI
25 Ashfahani, A., & Pratama, M. 2019, Autonomous Deep Learning: Continual Learning Approach for Dynamic Environments, In Proceedings of the 2019 SIAM International Conference on Data Mining, Society for Industrial and Applied Mathematics, pp.666-674. https://doi.org/10.1137/1.9781611975673.75
26 Bengio, Y., Courville, A., & Vincent, P. 2013, Representation learning: A review and new perspectives, IEEE transactions on pattern analysis and machine intelligence, 35, 1798-1828. https://doi.org/10.1109/TPAMI.2013.50   DOI