Abstract
Millimetre wave communication and massive MIMO are eminent candidates, to meet the requirements in a 5G network. The Combination of these techniques will impart an innovative and efficient transceiver signal processing techniques and radio resource management. The main idea of this paper is to propose a joint framework for user scheduling and hybrid precoder using Machine Learning (ML) for downlink Multi-User Multiple Input and Multiple Output systems (MU-MIMO) to enhance the sum rate. The downlink Multi-User MIMO (MU-MIMO) system model has been implemented in the Keysight’s Electronic System Level (ESL) SystemVue software to generate a channel matrix. Using the simulated channel matrix, correlation among the users were calculated which represents the realistic 5G channel response. A joint framework has been implemented with the Correlation factor-based user scheduling and cross entropy-based hybrid precoder (JUSHP) to maximize the achievable sum rate. The paper also proposes a weight assigned Cross- Entropy based hybrid precoder (JUSWHP) for further improvement in achievable sum rate. In both the framework machine learning algorithm has been applied to choose the best precoder for users in the MU-MIMO system. The simulation results authenticate that the JUSWHP framework achieves better performance with respect to achievable Sum Rate when compared to the other techniques.
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Acknowledgements
This research work is supported by the All India Council for Technical Education (AICTE), New Delhi, India, under RPS-NDF (Grant No: 8-3/RIFD/RPS-NDF/Policy-1/2018-2019).
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Rajarajeswarie, B., Sandanalakshmi, R. Machine learning based hybrid precoder with user scheduling technique for maximizing sum rate in downlink MU-MIMO system. Int. j. inf. tecnol. 14, 2399–2405 (2022). https://doi.org/10.1007/s41870-022-00902-3
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DOI: https://doi.org/10.1007/s41870-022-00902-3