Abstract
Spectrum sensing is essential for future wireless communications, distinguishing 5G, Long-Term Evolution (LTE), and noise signals. It ensures effective coexistence by identifying and allocating available spectrum for diverse technologies. This capability optimizes network performance, mitigates interference, and facilitates seamless integration of IoT devices, enhancing overall communication efficiency and reliability. Recently deep learning (DL) architectures are vital for classifying 5G new radio (5G NR), LTE, and noise signals in wireless communication. They automate feature extraction, improving accuracy and spectrum management for seamless coexistence of diverse technologies. The paper presents two deep neural network architectures, Convolutional Neural Network (CNN) and VGG16, designed to discern between 5G NR and LTE signals. The primary objective is to enhance spectrum awareness by proficiently identifying distinct signal patterns within a cellular system environment. A comprehensive performance analysis of classifiers is conducted, leveraging with different optimizers. Additionally, the research investigates the impact of varying training rates on the classifiers’ efficacy, contributing insights into their comparative superiority.
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References
Huynh-The, T., Pham, Q.-V., Nguyen, T.-V., da Costa, D.B., Kim, D.-S.: Racomnet: high-performance deep network for waveform recognition in coexistence radar-communication systems. In: 2022 IEEE International Conference on Communications (ICC), pp. 1–6 (2022). https://doi.org/10.1109/ICC.2022.9781658
Pham, Q.-V., Nguyen, N.T., Huynh-The, T., Le, L.B., Lee, K., Hwang, W.-J.: Intelligent radio signal processing: a survey. IEEE Access 9, 83818–83850 (2021). https://doi.org/10.1109/ACCESS.2021.3066589
Gao, J., Yi, X., Zhong, C., Chen, X., Zhang, Z.: Deep learning for spectrum sensing. IEEE Wireless Communications Letters 8(6), 1727–1730 (2019). https://doi.org/10.1109/LWC.2019.2937971
Venkata Subbarao, M., Samundiswary, P.: Performance analysis of modulation recognition in multipath fading channels using pattern recognition classifiers. Wireless Pers. Commun. 115, 129–151 (2020). https://doi.org/10.1007/s11277-020-07564-z
Venkata Subbarao, M., Keerthana, B., Ramesh Varma, D., Terlapu, S.K., Challa Ram, G.: Automatic modulation classification under AWGN and fading channels using convolutional neural network. In: Chakravarthy, V., Bhateja, V., Flores Fuentes, W., Anguera, J., Vasavi, K.P. (eds.) Advances in Signal Processing, Embedded Systems and IoT. Lecture Notes in Electrical Engineering, vol. 992, Springer, Singapore (2023). https://doi.org/10.1007/978-981-19-8865-3_20
Venkata Subbarao, M., Samundiswary, P.: Spectrum sensing in cognitive radio networks using time–frequency analysis and modulation recognition. In: Anguera, J., Satapathy, S., Bhateja, V., Sunitha, K. (eds.) Microelectronics, Electromagnetics and Telecommunications. Lecture Notes in Electrical Engineering, vol. 471, Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-7329-8_85
Huynh-The, T., Pham, Q.-V., Vu, T.-H., da Costa, D.B., Hoang, V.-P.: Intelligent spectrum sensing with convnet for 5G and LTE signals identification. In: 2023 IEEE Statistical Signal Processing Workshop (SSP), pp. 140–144, Hanoi, Vietnam (2023)
Alhazmi, M.H., Alymani, M., Alhazmi, H., Almarhabi, A., Samarkandi, A., Yao, Y.-D.: 5G signal identification using deep learning. In: 2020 29th Wireless and Optical Communications Conference (WOCC), pp. 1–5, Newark, NJ, USA (2020). https://doi.org/10.1109/WOCC48579.2020.9114912
Reddy, M.M.K., Monisha, M.: Enhanced deep learning architectures for spectrum sensing in cellular networks. In: 2023 8th International Conference on Communication and Electronics Systems (ICCES), pp. 1285–1290, Coimbatore, India (2023). https://doi.org/10.1109/ICCES57224.2023.10192889
Biswas, A., Gupta, V.R.: Multiband antenna design for smartphone covering 2G, 3G, 4G and 5G NR frequencies. In: 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), pp. 84–87, Tirunelveli, India (2019). https://doi.org/10.1109/ICOEI.2019.8862713
Ram, G.C., Venkata Subbarao, M., Ramesh Varma, D., Krishna, A.S.: Enhanced deep convolutional neural network for identifying and classification of silicon wafer faults in IC fabrication industries. In: 2023 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET), pp. 1–6, Chennai, India (2023). https://doi.org/10.1109/WiSPNET57748.2023.10133996
Venkata Subbarao, M., Sindhu, J.T.S., Harshitha, N.N.S., Vasavi, K.P., Krishna, A.S., Ram, G.C.: Detection of retinal degeneration via high-resolution fundus images using deep neural networks. In: 2023 Second International Conference on Electronics and Renewable Systems (ICEARS), pp. 955–960, Tuticorin, India (2023). https://doi.org/10.1109/ICEARS56392.2023.10085273
Xie, J., Fang, J., Liu, C., Li, X.: Deep learning-based spectrum sensing in cognitive radio: a CNN-LSTM approach. IEEE Commun. Lett. 24(10), 2196–2200 (2020). https://doi.org/10.1109/LCOMM.2020.3018194
Zhang, R., Lim, T.J., Liang, Y., Zeng, Y.: Multi-antenna based spectrum sensing for cognitive radios: a glrt approach. IEEE Trans. Commun. 58(1), 84–88 (2010). https://doi.org/10.1109/TCOMM.2009.12.080594
Nalli, P.K., Venkata Subbarao, M., Garapati, D.P., Priyakanth, R., Kumar, G.P.: Performance analysis of pre-trained deep learning architectures for classification of corn leaf diseases. In: 2023 International Conference on Network, Multimedia and Information Technology (NMITCON), pp. 1–8, Bengaluru, India (2023). https://doi.org/10.1109/NMITCON58196.2023.10275915
KrishnaKishore, K., VenkataSuman, J., Madhavi, M., Hema, M., Venkataramana, G.: Scrambled UFMC and OFDM techniques with APSK modulation in 5G networks using particle swarm optimization. IEEE Access. (2024). https://doi.org/10.1109/ACCESS.2024.3421311.
Kishore, K.K., Rajasekaran, A.S., Keshta, I., et al.: Intelligent dynamic spectrum access using fuzzy logic in cognitive radio networks. Discov Appl Sci 6, 18 (2024). https://doi.org/10.1007/s42452-024-05641-7
Krishna Kishore, K., Sri, D.K., Naveen, J., et al.: Performance evaluation of universal filter multi carrier (UFMC) and orthogonal frequency division multiplexing (OFDM) with amplitude phase shift keying (APSK) modulation. PREPRINT (Version 1) available at Research Square (2023). https://doi.org/10.21203/rs.3.rs-3155601/v1
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Mani Kumar Reddy, M., Monisha, M. (2026). Enhancing Spectrum Awareness in Cellular Networks Through Deep Learning Approaches for Efficient 5G-NR and LTE Signal Classification. In: Koganti, K.K., E., S.R., Gupta, N. (eds) Advanced Technologies in Electronics, Communications and Signal Processing. ICATECS 2024. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 620. Springer, Cham. https://doi.org/10.1007/978-3-031-94283-9_23
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