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Design of Hybrid Beamforming for Multiuser MIMO mmWave Systems Using Deep Learning

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Abstract

Beamforming (BF) architecture is a key task for the next deades communication systems and also is an developing approach for large-scale antenna arrays. The proposed approach in fully connected analog phase-shifter is millimeter wave transmission suited BF architecture with CSI (channel state information) and limited radio frequency chains. DL (Deep learning) is a vigorous approach for signal identification and channel estimation into wireless communications. Hence, the proposed the DL-allowed BFNN (beamforming neural network) which can be designed to optimize the multi user massive mimo system to get higher Spectral Efficiency. Simulation results shows the proposed beamforming neural network acts on consequential perform high robustness and gain to imperfect CSI.The BFNN proposed enormously decreas the computational complexity hardby 0.18 millions Floating Point Operations (FLOPs) accomplished 0.28 millions floating point operations by conventional beamforming algorithms.

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Authors and Affiliations

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

    Sammaiah Thurpati & P. Muthuchidambaranathan

  2. Electronics and Communication Engineering, Jayamukhi Institute of Technological Sciences, Warangal, Telangana, 506332, India

    Mahesh Mudavath

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  1. Sammaiah Thurpati
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Correspondence to Sammaiah Thurpati.

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1. All authors have participated in (a) conception and design, or analysis and interpretation of the data; (b) drafting the article or revising it critically for important intellectual content; and (c) approval of the final version. 2. This manuscript has not been submitted to, nor is under review at, another journal or other publishing venue. 3. Authors have no Conflict of interest.

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Thurpati, S., Mudavath, M. & Muthuchidambaranathan, P. Design of Hybrid Beamforming for Multiuser MIMO mmWave Systems Using Deep Learning. Wireless Pers Commun 135, 1747–1760 (2024). https://doi.org/10.1007/s11277-024-11159-3

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