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Extreme learning machine and correntropy criterion-based hybrid precoder for 5G wireless communication systems

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Abstract

Application of massive multiple input multiple output (mMIMO) in millimeter wave (mmWave) band is a promising solution for 5G communication due to low latency and directional beamforming. Hybrid precoding is an integral part of 5G systems to fully exploit spatial information in presence of large path loss with reduction of RF chains. However, hybrid precoder design is a challenging research concern due to the involvement of large number of antennas in transmitter and receiver. Additionally, higher spectral efficiency in the presence of impulsive noise in FR2 or mmWave band also requires attention. ELM (extreme learning machine) has an efficient feed forward neural architecture with only one hidden layer which is suitable to design efficient precoding models. Therefore, this research focuses on to design hybrid precoder in millimeter wave band using ELM and variable center correntropy criterion to obtain higher spectral efficiency. Exhaustive simulation results indicate that the proposed precoder has significantly better performance than state-of-art methods in multiple test conditions. The precoder performance is tested by varying base station antennas, mobile station antennas and number of users. The bit error rate (BER) performance is also analyzed. and comparison results are presented to justify the claim.

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Acknowledgements

Authors acknowledge the support from VSSUT, Burla and KIIT University, Bhubaneswar for the support in terms of E-journals, library and the laboratories.

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Authors and Affiliations

  1. School of Electronics Engineering, KIIT, Bhubaneswar, India

    Swetaleena Sahoo & Sarita Nanda

  2. Department of Electronics and Telecommunication Engineering, VSSUT, Burla, Odisha, India

    Harish Kumar Sahoo & Subhashree Samal

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  1. Swetaleena Sahoo
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  2. Harish Kumar Sahoo
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  3. Sarita Nanda
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  4. Subhashree Samal
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Contributions

Swetaleena Sahoo has done the mathematical modeling and simulation. Harish Kumar Sahoo and Sarita Nanda have done the necessary corrections and arrangement of sections in the submitted manuscript. Subhashree Samal has done a literature review on the topic.

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Correspondence to Harish Kumar Sahoo.

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Sahoo, S., Sahoo, H.K., Nanda, S. et al. Extreme learning machine and correntropy criterion-based hybrid precoder for 5G wireless communication systems. SIViP (2024). https://doi.org/10.1007/s11760-024-03205-1

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  • DOI: https://doi.org/10.1007/s11760-024-03205-1

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