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Channel estimation in 5G multi input multi output wireless communication using optimized deep neural framework

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Abstract

Channel estimation is essential in a Multiple Input Multiple Output (MIMO) wireless communication in 5G. In the MIMO system, numerous antennas are utilized on the sender and receiver sides for enhancing spectral efficiency and reliability. The channel estimation can improve the exactness of the received signal. Increasing the number of channel coefficients can make channel estimation fairly complex. Transmitting multiple paths have some delay and signal echoes. Therefore, channel assessment is very necessary for efficiently receiving the transmitted signals. A novel Enhanced Convolution Neural African Buffalo approach was developed for channel estimation purposes to overcome such issues. Moreover, the additive white Gaussian noise channel is created for MIMO communication. The simulation of this research is done using the network simulator platform. Sequentially, the proposed system outcomes are compared with other techniques in terms of throughput, accuracy, signal to noise ratio, mean square error, and bit error rate. The comparison results also proved the effectiveness of the proposed approach.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, B V Raju Institute of Technology, Narsapur, Telangana, India

    Prabhakara Rao Kapula

  2. Department of Electronics and Communication Engineering, A U College of Engineering, Visakhapatnam, Andhra Pradesh, India

    P. V. Sridevi

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  1. Prabhakara Rao Kapula
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Correspondence to Prabhakara Rao Kapula.

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Kapula, P.R., Sridevi, P.V. Channel estimation in 5G multi input multi output wireless communication using optimized deep neural framework. Cluster Comput 25, 3517–3530 (2022). https://doi.org/10.1007/s10586-022-03587-2

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