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- W4285065201 abstract "Massive multiple-input multiple-output (MIMO) plays an important role in future generation communication improving both energy and spectral efficiency. The non-orthogonal pilot sequences cause pilot contamination affecting performance of the system. Moreover, larger number of base station antennas increases the channel matrix dimension which affects pilot overhead and channel state information (CSI). To solve these problems, we propose an optimal channel estimation technique for multi-cell massive MIMO system using hybrid machine learning technique (OCE-HML). At first, we introduce a discrete bacterial optimization-based clustering (DBO) algorithm used to classify users into the cell-edge and cell-centre depending on their receiver side SINR, network throughput and distance threshold. Secondly, we illustrate capsule learning-based convolutional neural network (CCNN) to select best detection method for channel estimation based on cell-edge users SINRs. Cell-edge users with low SINR use Lanczos algorithm-based signal detection method by using different pilots to avoid interference and users with high SINR use minimum mean square error (MMSE) detection method to improve pilot efficiency use by reusing the same pilots. The simulation results show the proposed detectors perform much effectively compared to existing state-of-art detection techniques in terms of different performance metrics." @default.
- W4285065201 created "2022-07-13" @default.
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- W4285065201 date "2022-01-01" @default.
- W4285065201 modified "2023-10-18" @default.
- W4285065201 title "Optimal Pilot Contamination Mitigation-Based Channel Estimation for Massive MIMO System Using Hybrid Machine Learning Technique" @default.
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- W4285065201 doi "https://doi.org/10.1007/978-981-19-0825-5_33" @default.
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