Optimised deep learning–aided transmit antenna selection and beamforming for energy efficient MIMO system
Source abstract
Abstract
Beamforming optimisation methods stand as a crucial component for providing higher robustness and efficiency of wireless communication systems in the rapidly evolving landscape of mobile telecommunication. At first, essential data for the validation is collected by varying the sampling points, locations and user counts, and it is selected optimally using an Updated Random Attribute-based Cock-hen-chicken Optimiser (URA-CO). Once the required data for the validation is collected, then the Channel State Information (CSI) is considered for performing beamforming. Here, the Adaptive Stacked Sparse Autoencoder with Spatial Channel Attention (ASSA-SCA) method is employed to execute the beamforming and antenna selection process in the energy-efficient Fifth-Generation (5G) networks. The parameters in the developed ASSA-SCA are tuned using URA-CO that enhances the beamforming vector values and selects the most suitable antenna to transfer the information in the 5G networks. Various analyses are performed in the developed model over the classical techniques to prove its effectiveness.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced MIMO Systems Optimization · Advanced Wireless Communication Technologies · Wireless Signal Modulation Classification
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Paavai Engineering College
Sona College of Technology
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