Deep Learning for High-Frequency Network

Deep Learning for High-Frequency Network

© 2022 by IJETT Journal
Volume-70 Issue-5
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,

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.

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