Modified gate activation functions of Bi-LSTM-based SC-FDMA channel equalization

[1] S. Iqbal, A. H. Abdullah, and K. N. Qureshi, “Channel quality and utilization metric for interference estimation in Wireless Mesh Networks,” Computers & Electrical Engineering, vol. 64, pp. 420-435, 2017. Search in Google Scholar

[2] J. G. Proakis and M. Salehi, Digital communications. McGraw-hill New York, 2001. Search in Google Scholar

[3] N. Benvenuto, G. Cherubini, and S. Tomasin, Algorithms for communications systems and their applications. John Wiley & Sons, 2021. Search in Google Scholar

[4] R. v. Nee and R. Prasad, “OFDM for wireless multimedia communications,” 2000. Artech House, Inc. Search in Google Scholar

[5] I. Jeanclaude and G. K. Sari, “An Analysis of Orthogonal Frequency-Division Multiplexing for Mobile Radio Applications,” IEEE Transactions 0-7803-1927-3/94, pp. 1635-1639, 1994. Search in Google Scholar

[6] M. Anbar, N. Iqbal, A. Zerguine, and M. S. Alouini, “Iterative SC-FDMA Frequency Domain Equalization and Phase Noise Mitigation,” in 2018 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), 2018, pp. 91-95. Search in Google Scholar

[7] K. Burse, R. N. Yadav, and S. Shrivastava, “Channel equalization using neural networks: A review,” IEEE transactions on systems, man, and cybernetics, Part C (Applications and Reviews), vol. 40, no. 3, pp. 352-357, 2010. IEEE Search in Google Scholar

[8] J. C. Patra, W. C. Chin, P. K. Meher, and G. Chakraborty, “Legendre-FLANN-based nonlinear channel equalization in wireless communication system,” in 2008 IEEE International Conference on Systems, Man and Cybernetics, 2008, pp. 1826-1831: IEEE.Search in Google Scholar

[9] C.-Y. Lo, “Application of neural network techniques on nonlinear channel equalization for 16-QAM modulation systems,” in 2008 Eighth International Conference on Intelligent Systems Design and Applications, 2008, vol. 1, pp. 356-361: IEEESearch in Google Scholar

[10] T. Adali and M. Sonmez, “Channel equalization with perceptrons: An information-theoretic approach,” in Proceedings of ICASSP’94. IEEE International Conference on Acoustics, Speech and Signal Processing, 1994, vol. 3, pp. III/297-III/300 vol. 3: IEEE.Search in Google Scholar

[11] R.-C. Huang and M.-S. Chen, “Adaptive equalization using complex-valued multilayered neural network based on the extended Kalman filter,” in WCC 2000-ICSP 2000. 2000 5th International Conference on Signal Processing Proceedings. 16th World Computer Congress 2000, 2000, vol. 1, pp. 519-524: IEEE. Search in Google Scholar

[12] H. Ye and G. Y. Li, “Initial results on deep learning for joint channel equalization and decoding,” in 2017 IEEE 86th Vehicular Technology Conference (VTC-Fall), 2017, pp. 1-5: IEEE. Search in Google Scholar

[13] E. Giacoumidis, A. Tsokanos, M. Ghanbarisabagh, S. Mhatli, and L. P. Barry, “Unsupervised Support Vector Machines for Nonlinear Blind Equalization in COOFDM,” IEEE Photonics Technology Letters, vol. 30, no. 12, pp. 1091-1094, 2018. Search in Google Scholar

[14] A. Caciularu and D. Burshtein, “Blind channel equalization using variational autoencoders,” in 2018 IEEE International Conference on Communications Workshops (ICC Workshops), 2018, pp. 1-6: IEEE. Search in Google Scholar

[15] J. Wang and Z. Han, “Research on Speech Emotion Recognition Technology based on Deep and Shallow Neural Network,” in 2019 Chinese Control Conference (CCC), 2019, pp. 3555-3558. Search in Google Scholar

[16] S. Yang, “Natural Language Processing Based on Convolutional Neural Network and Semi Supervised Algorithm in Deep Learning,” in 2022 International Conference on Artificial Intelligence in Everything (AIE), 2022, pp. 174-178. Search in Google Scholar

[17] J. Zhang, L. Sang, Z. Wan, Y. Wang, and Y. Li, “Deep Convolutional Neural Network Based on Multi-Scale Feature Extraction for Image Denoising,” in 2020 IEEE International Conference on Visual Communications and Image Processing (VCIP), 2020, pp. 213-216. Search in Google Scholar

[18] N. Jmour, S. Zayen, and A. Abdelkrim, “Convolutional neural networks for image classification,” in 2018 International Conference on Advanced Systems and Electric Technologies (IC_ASET), 2018, pp. 397-402. Search in Google Scholar

[19] T. O’shea and J. Hoydis, “An Introduction to Deep Learning for the Physical Layer,” IEEE Transactions on Cognitive Communications and Networking, vol. 3, no. 4, pp. 563-575, 2017. IEEE Search in Google Scholar

[20] H. Huang et al., “Deep Learning for Physical-Layer 5G Wireless Techniques: Opportunities, Challenges and Solutions,” IEEE Wireless Communications, vol. 27, no. 1, pp. 214-222, 2020. Search in Google Scholar

[21] J. Dai, P. Zhang, J. Mazumdar, R. G. Harley, and G. Venayagamoorthy, “A comparison of MLP, RNN and ESN in determining harmonic contributions from nonlinear loads,” in 2008 34th Annual Conference of IEEE Industrial Electronics, 2008, pp. 3025-3032: IEEE. Search in Google Scholar

[22] A. Graves and J. Schmidhuber, “Framewise phoneme classification with bidirectional LSTM and other neural network architectures,” Neural networks, vol. 18, no. 5-6, pp. 602-610, 2005. ElsevierSearch in Google Scholar

[23] A. Graves, A.-r. Mohamed, and G. Hinton, “Speech recognition with deep recurrent neural networks,” in 2013 IEEE international conference on acoustics, speech and signal processing, 2013, pp. 6645-6649: Ieee. Search in Google Scholar

[24] W. Xu, Z. Zhong, Y. Be’ery, X. You, and C. Zhang, “Joint neural network equalizer and decoder,” in 2018 15th International Symposium on Wireless Communication Systems (ISWCS), 2018, pp. 1-5: IEEE. Search in Google Scholar

[25] X.-H. Le, H. V. Ho, G. Lee, and S. Jung, “Application of long short-term memory (LSTM) neural network for flood forecasting,” Water, vol. 11, no. 7, p. 1387, 2019. MDPI Search in Google Scholar

[26] L. Rahman, N. Mohammed, and A. K. Al Azad, “A new LSTM model by introducing biological cell state,” in 2016 3rd International Conference on Electrical Engineering and Information Communication Technology (ICEEICT), 2016, pp. 1-6: IEEE. Search in Google Scholar

[27] A. Graves, N. Jaitly, and A.-r. Mohamed, “Hybrid speech recognition with deep bidirectional LSTM,” in 2013 IEEE workshop on automatic speech recognition and understanding, 2013, pp. 273-278: IEEE. Search in Google Scholar

[28] M. H. E. Ali, A. B. Abdel-Raman, and E. A. J. I. A. Badry, “Developing Novel Activation Functions Based Deep Learning LSTM for Classification,” vol. 10, pp. 97259-97275, 2022. Search in Google Scholar

[29] P. Han, W. Wang, Q. Shi, and J. Yang, “Real-time short-term trajectory prediction based on GRU neural network,” in 2019 IEEE/AIAA 38th Digital Avionics Systems Conference (DASC), 2019, pp. 1-8: IEEE. Search in Google Scholar

[30] M. M. Rahman, Y. Watanobe, and K. Nakamura, “A bidirectional LSTM language model for code evaluation and repair,” Symmetry, vol. 13, no. 2, p. 247, 2021. MDPISearch in Google Scholar

[31] G. S. d. S. Gomes and T. B. Ludermir, “Optimization of the weights and asymmetric activation function family of neural network for time series forecasting,” Expert Systems with Applications, vol. 40, no. 16, pp. 6438-6446, 2013. Elsevier Search in Google Scholar

[32] Y. Singh and P. Chandra, “A class+ 1 sigmoidal activation functions for FFANNs,” Journal of Economic Dynamics and Control, vol. 28, no. 1, pp. 183-187, 2003. Elsevier Search in Google Scholar

[33] A. Farzad, H. Mashayekhi, and H. Hassanpour, “A comparative performance analysis of different activation functions in LSTM networks for classification,” Neural Computing and Applications, vol. 31, no. 7, pp. 2507-2521, 2019. Springer Search in Google Scholar

[34] E. Dogo, O. Afolabi, N. Nwulu, B. Twala, and C. Aigbavboa, “A comparative analysis of gradient descent-based optimization algorithms on convolutional neural networks,” in 2018 international conference on computational techniques, electronics and mechanical systems (CTEMS), 2018, pp. 92-99: IEEE. Search in Google Scholar