Deep Learning for High-Frequency Network
Deep Learning for High-Frequency Network
|© 2022 by IJETT Journal|
|Year of Publication : 2022|
|Authors : Mrinalini , Kamlesh Kumar Singh , Himanshu Katiyar
|DOI : 10.14445/22315381/IJETT-V70I5P230|
How to Cite?
Mrinalini , Kamlesh Kumar Singh , Himanshu Katiyar, "Deep Learning for High-Frequency Network," International Journal of Engineering Trends and Technology, vol. 70, no. 5, pp. 274-284, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I5P230
Recurrent neural networks (RNNs) are neural network that is often employed to process sequential input. RNNs are infamously challenging to train and learn persistent patterns due to the well-known gradient disappearing and explosion difficulties. Long short-term memory (LSTM) and Gated recurrent unit (GRU) was developed to resolve these problems. However, when applied to these devices, the usage of sigmoid action functions and hyperbolic tangents causes gradient degradation across layers. Therefore, constructing a deep trainable network is very challenging. Rectified linear unit (RELU)activation may be used by an IndRNN for non-saturated activation purposes while still being reliably trained. IndRNNs may be layered on top of one another to create a greater network than the current RNNs. Furthermore, in an RNN layer, all the neurons are intertwined, making it difficult to decipher their behavior. In this paper, the base paper technique is deep learning and is compared with other techniques to find out the most optimized, and in the implementation, the bit error rate of the technique is determined.
Deep Learning, GFDM, Natural language processing, OFDM, and Recurrent neural networks.
 Dupond, Samuel. A Thorough Review on the Current Advance of Neural Network Structures, Annual Reviews in Control. 14 (2019) 200-230.
 Abiodun, Oludare Isaac, AmanJantan, Abiodun Esther Omolara, Kemi Victoria Dada, NachaatAbdElatif Mohamed, and HumairaArshad, State-of-the-Art in Artificial Neural Network Applications: A Survey, Heliyon. 4(11) (2018) e00938.
 Tealab, Ahmed, Time Series Forecasting Using Artificial Neural Networks Methodologies: A Systematic Review, Future Computing and Informatics Journal. 3(2) (2018) 334-340.
 Graves, Alex, Marcus Liwicki, Santiago Fernández, Roman Bertolami, Horst Bunke, and Jürgen Schmidhuber, A Novel Connectionist System for Unconstrained Handwriting Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence. 31(5) (2008) 855-868.
 Sak, Hasim, Andrew W. Senior, and Françoise Beaufays, Long Short-Term Memory Recurrent Neural Network Architectures for Large Scale Acoustic Modeling. (2014).
 Li, Xiangang, and Xihong Wu, Constructing Long Short-Term Memory Based Deep Recurrent Neural Networks for Large Vocabulary Speech Recognition, In 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE. (2015) 4520-4524.
 Cabessa, Jérémie, and Alessandro EP Villa, The Expressive Power of Analog Recurrent Neural Networks on Infinite Input Streams, Theoretical Computer Science. 436 (2012) 23-34.
 Miljanovic, Milos. Comparative Analysis of Recurrent and Finite Impulse Response Neural Networks in Time Series Prediction, Indian Journal of Computer Science and Engineering. 3(1) (2012) 180-191.
 Concepcion II, Ronnie, Elmer Dadios, Joel Cuello, Argel Bandala, Edwin Sybingco, and Ryan Rhay Vicerra, Determination of Aquaponic Water Macronutrient Concentrations Based on Lactuca Sativa Leaf Photosynthetic Signatures using Hybrid Gravitational Search and Recurrent Neural Network, Walailak Journal of Science and Technology (WJST). 18(10) (2021) 18273-20.
 Li, Shuai, Wanqing Li, Chris Cook, Ce Zhu, and YanboGao, Independently Recurrent Neural Network (INDRNN): Building A Longer And Deeper RNN, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. (2018) 5457-5466.
 Sanayha, Manassakan, and Peerapon Vateekul, Remaining Useful Life Prediction Using Enhanced Convolutional Neural Network on Multivariate Time Series Sensor Data, Walailak Journal of Science and Technology (WJST). 16(9) (2019) 669-679.
 Goodfellow, Ian, YoshuaBengio, Aaron Courville, and YoshuaBengio, Deep Learning, Cambridge: MIT Press. 1(2) (2016).
 Kumar, S. Lokesh, Predictive Analytics of COVID-19 Pandemic: Statistical Modelling Perspective, Walailak Journal of Science and Technology (WJST). 18(16) (2021) 15583-14.
 Yang, Yuwen, FeifeiGao, Xiaoli Ma, and Shun Zhang, Deep Learning-Based Channel Estimation for Doubly Selective Fading Channels, IEEE Access. 7 (2019) 36579-36589. Doi:10.1109/ACCESS.2019.2901066.
 O`Shea, Timothy J., KiranKarra, and T. Charles Clancy, Learning to Communicate: Channel Auto-Encoders, Domain-Specific Regularizers, and Attention, In IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), IEEE. (2016) 223-228. Doi:10.1109/ISSPIT.2016.7886039
 Huang, Qisheng, Chunming Zhao, Ming Jiang, Xiaoming Li, and Jing Liang. Cascade-Net: A New Deep Learning Architecture for OFDM Detection, arXiv Preprint arXiv:1812.00023. (2018).
 Zhao, Zhongyuan, Mehmet C. Vuran, FujuanGuo, and Stephen Scott. Deep waveform: A learned OFDM Receiver Based on Deep, Complex Convolutional Networks. arXiv preprint arXiv:1810.07181. (2018). Doi:10.1109/PIMRC.2019.8904193.
 Osia, Seyed Ali, Ali ShahinShamsabadi, SinaSajadmanesh, Ali Taheri, KleomenisKatevas, Hamid R. Rabiee, Nicholas D. Lane, and HamedHaddadi. A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics, IEEE Internet of Things Journal. 7(5) (2020) 4505-4518. Doi: 10.1109/jiot.2020.2967734
 Mao, Qian, Fei Hu, and Qi Hao, Deep Learning for Intelligent Wireless Networks: A Comprehensive Survey, IEEE Communications Surveys & Tutorials. 20(4) (2018) 2595-2621. Doi: 10.1109/COMST.2018.2846401
 Gaurav, and PratisthaMathur, An Attention Mechanism and GRU Based Deep Learning Model for Automatic Image Captioning, International Journal of Engineering Trends and Technology. 70(3) (2022) 302-309.
 Jordan, Michael I. Serial order: A Parallel Distributed Processing Approach, In Advances in Psychology, North-Holland. 121 (1997) 471-495. Doi: 10.1002/MP.14140
 Donahue, Jeffrey, Lisa Anne Hendricks, Sergio Guadarrama, Marcus Rohrbach, SubhashiniVenugopalan, Kate Saenko, and Trevor Darrell, Long-Term Recurrent Convolutional Networks for Visual Recognition and Description, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. (2015) 2625-2634. Doi:10.1109/CVPR.2015.7298878
 Byeon, Wonmin, Thomas M. Breuel, Federico Raue, and Marcus Liwicki, Scene Labeling with LSTM Recurrent Neural Networks, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. (2015) 3547-3555. Doi: 10.1109/CVPR.2015.7298977
 Cho, Kyunghyun, Bart Van Merriënboer, CaglarGulcehre, DzmitryBahdanau, FethiBougares, HolgerSchwenk, and YoshuaBengio, Learning Phrase Representations Using RNN Encoder-Decoder for Statistical Machine Translation, arXiv Preprint arXiv:1406.1078. (2014).
 Hashi, A. O., O. E. R. Rodriguez, A. A. Abdirahman, and M. M. Mohamed, Collecting Targeted Information About Covid-19 From Research Papers by Asking Questions Based on Natural Language Processing, International Journal of Engineering Trends and Technology. (2021) 190-195.
 Karpathy, Andrej, Justin Johnson, and Li Fei-Fei, Visualizing and Understanding Recurrent Networks, arXiv Preprint arXiv:1506.02078. (2015).
 Sharma, Mahendra, and Santhosh Kumar Singh, Orthogonality Measurement of OFDM Signal, Indonesian Journal of Electrical Engineering and Computer Science. 9(3) (2018) 595-598. Doi:10.11591/ijeecs.v9.i3.pp595-598
 Farhang-Boroujeny, Behrouz. OFDM Versus Filter Bank Multicarrier, IEEE Signal Processing Magazine. 28(3) (2011) 92-112. Doi:10.1109/MSP.2011.940267
 Vakilian, Vida, Thorsten Wild, Frank Schaich, Stephan ten Brink, and Jean-Francois Frigon, Universal-Filtered Multi-Carrier Technique for Wireless Systems Beyond LTE, In IEEE Globecom Workshops (GC Wkshps), IEEE. (2013) 223-228. Doi:10.1109/glocomw.2013.6824990
 Arjun, K. R., and T. P. Surekha, Peak-to-Average Power Ratio Reduction in Wavelet based OFDM using Modified Selective Mapping for Cognitive Radio Applications, Walailak Journal of Science and Technology (WJST). 18(12) (2021) 19814-12.
 Rahman, Mohammad Mizanur, Chalie Charoenlarpnopparut, Prapun Suksompong, and Attaphongse Taparugssanagorn, Correlation Coefficient Based DVB-T Continual Pilot Detection to Identify Spectrum Hole for CR Application, Walailak Journal of Science and Technology (WJST). 13(6) (2016) 479-490.
 Sorooshian, Shahryar, And Shrikant Panigrahi, Impacts of the 4th Industrial Revolution on Industries, Walailak Journal of Science and Technology (WJST). 17(8) (2020) 903-915.
 Tai, Ching-Lun, Tzu-Han Wang, and Yu-Hua Huang, An Overview of Generalized Frequency Division Multiplexing (GFDM). arXiv Preprint arXiv:2008.08947. (2020). Doi:10.36227/techrxiv.16432992.v1
 Chen, Chen, Lin Zeng, XinZhong, Shu Fu, Min Liu, and Pengfei Du. Deep Learning-Aided OFDM-Based Generalized Optical Quadrature Spatial Modulation. arXiv Preprint arXiv:2106.12770. (2021). Doi: 10.1167/tvst.10.9.18.
 Jiang, Rongkun, ZesongFei, Shan Cao, ChengboXue, Ming Zeng, Qingqing Tang, and ShiweiRen, Deep Learning-Aided Signal Detection for Two-Stage Index Modulated Universal Filtered Multi-Carrier Systems, IEEE Transactions on Cognitive Communications and Networking. (2021). Doi: 10.6038/cjg2020O0031
 Fong, Simon James, Gloria Li, NilanjanDey, Rubén González Crespo, and Enrique Herrera-Viedma, Finding an Accurate Early Forecasting Model from Small Dataset: A Case of 2019-ncov Novel Coronavirus Outbreak, arXiv Preprint arXiv:2003.10776. (2020).
 Li, Shuai, LongfeiZheng, Ce Zhu, and YanboGao, Bidirectional Independently Recurrent Neural Network for Skeleton-Based Hand Gesture Recognition, In 2020 IEEE International Symposium on Circuits and Systems (ISCAS), IEEE. (2020) 1-5. Doi: 10.1109/ISCAS45731.2020
 Turhan, Merve, E. Ozturk, and H. Cirpan. Deep Learning Aided Generalized Frequency Division Multiplexing, In Proc. 3rd Intl. Balkan Conf. Commun. Networking. (2019). Doi:10.1109/PIMRC.2019.8904193
 Li, Shuai, Wanqing Li, Chris Cook, and YanboGao, Deep Independently Recurrent Neural Network (INDRNN), arXiv Preprint arXiv:1910.06251. (2019).
 S. Soltani, Mehran, VahidPourahmadi, Ali Mirzaei, and Hamid Sheikhzadeh, Deep Learning-Based Channel Estimation, IEEE Communications Letters. 23(4) (2019) 652-655. Doi: 10.1109/LCOMM.2019.2898944
 Turhan, Merve, E. Ozturk, and H. Cirpan, Deep Learning Aided Generalized Frequency Division Multiplexing, In Proc. 3rd Intl. Balkan Conf. Commun. Networking. (2019). Doi:10.1109/PIMRC.2019.8904193
 Shi, Lei, Yifan Zhang, Jian Cheng, and Hanqing Lu, Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition, In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. (2019) 12026-12035. Doi: 10.1109/CVPR.2019.01230
 Öztürk, Ersin, ErtugrulBasar, and Hakan Ali Ç?rpan, Generalized Frequency Division Multiplexing with Flexible Index Modulation Numerology, IEEE Signal Processing Letters. 25(10) (2018) 1480-1484. Doi:10.1109/LSP.2018.2864601
 Le-Tran, Manh, and Sunghwan Kim, Deep Learning-Assisted Index Estimator for Generalized LED Index Modulation OFDM in Visible Light Communication, In Photonics, Multidisciplinary Digital Publishing Institute. 8(5) (2021) 168.
 Wu, Xi, Zhitong Huang, and Yuefeng Ji, Deep Neural Network Method for Channel Estimation in Visible Light Communication, Optics Communications. 462 (2020) 125272.
 Ozgur, Ceyhun, Taylor Colliau, Grace Rogers, and Zachariah Hughes, MatLab vs. Python vs. R, Journal of Data Science 15(3) (2017) 355-371.