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Analysis of Offset Quadrature Amplitude Modulation in FBMC for 5G Mobile Communication

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Proceedings of International Conference on Artificial Intelligence and Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1164))

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Abstract

Next-generation mobile communication is required to allocate the resources to all users smoothly and efficiently. But the allocation of resources is difficult in present 4G mobile communication systems using OFDM and hence in this manuscript, a new technique based on Filter Bank Multicarrier (FBMC) modulation and demodulation are designed and employed. The proposed scheme will help devices to communicate automatically employing machine learning techniques including higher data rates as per the need of users with less bit error rate. Offset quadrature amplitude modulation (OQAM) technique for FBMC is discussed and analyzed to achieve a lower bit error rate for the latest generation, 5G wireless communication

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Authors and Affiliations

  1. National Institute of Technology Delhi, Delhi, 110 040, India

    Ayush Kumar Agrawal & Manisha Bharti

Authors
  1. Ayush Kumar Agrawal
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  2. Manisha Bharti
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Corresponding author

Correspondence to Ayush Kumar Agrawal .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Maharaja Surajmal Institute of Technology, New Delhi, India

    Poonam Bansal

  2. Department of Electrical and Electronics Engineering, Maharaja Surajmal Institute of Technology, New Delhi, India

    Meena Tushir

  3. Department of Automation and Applied Software, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  4. Department of Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi, India

    Rajeev Srivastava

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Agrawal, A.K., Bharti, M. (2021). Analysis of Offset Quadrature Amplitude Modulation in FBMC for 5G Mobile Communication. In: Bansal, P., Tushir, M., Balas, V., Srivastava, R. (eds) Proceedings of International Conference on Artificial Intelligence and Applications. Advances in Intelligent Systems and Computing, vol 1164. Springer, Singapore. https://doi.org/10.1007/978-981-15-4992-2_53

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