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Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication pp 59–77Cite as

Novel Method of Self-interference Cancelation in Full-Duplex Radios for 5G Wireless Technology Using Neural Networks

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Part of the Lecture Notes in Electrical Engineering book series (LNEE,volume 749)

Abstract

Full-duplex communication is a promising technique which guarantees an enhanced spectral efficiency in modern 5G wireless communications. In this technique, same set of frequency channels is used for simultaneous uplink and downlink signal transmissions and hence is termed as full-duplex (FD) communications or full-duplex radios. However, a major shortcoming of this technique is the presence of self-interference (SI), which arises due to the presence of both transmitters and receivers in close proximity and in fact several solutions have been proposed to mitigate it. In this paper, we give a new insight on the applicability of neural networks in solving (linear and nonlinear) SI problems using hybrid cancelations.

Keywords

  • Full-duplex radios
  • Self-interference
  • Hybrid cancelation
  • Neural networks

All authors have given equal contribution.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, National Institute of Technology, Tiruchirappalli, 620015, India

    L. Yashvanth, V. Dharanya & E. S. Gopi

Authors
  1. L. Yashvanth
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  2. V. Dharanya
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  3. E. S. Gopi
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Corresponding author

Correspondence to L. Yashvanth .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, National Institute of Technology Tiruchirappalli, Tiruchirappalli, Tamil Nadu, India

    Dr. E. S. Gopi

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Yashvanth, L., Dharanya, V., Gopi, E.S. (2021). Novel Method of Self-interference Cancelation in Full-Duplex Radios for 5G Wireless Technology Using Neural Networks. In: Gopi, E.S. (eds) Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication. Lecture Notes in Electrical Engineering, vol 749. Springer, Singapore. https://doi.org/10.1007/978-981-16-0289-4_5

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