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Optimized beamforming vector and antenna selection using autoencoder with Bayesian RNN and hybrid heuristic algorithm for MIMO systems

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2025年第3期38卷

作者： Thirupathi, Sivakumar Ranganathan, Kavitha Krishnasamy Sona Coll Technol ECE Salem 636005 Tamil Nadu India

Wireless communication systems depend on channel state information (CSI). However, the CSI collection is a significant barrier to achieving high performance. Present wireless communication models mainly rely on many antennas and related signal dispensation for increasing effectiveness. User mobility, the usage of greater frequencies, and a better number of antennas contribute to beamforming and beam management in these networks. Therefore, an improved hybrid heuristic algorithm is proposed here for providing optimized Joint beam forming and antenna selection (JBAS) in a multi-input multi-output (MIMO) model. The major novelty of the research work is to jointly perform the beam forming and antenna selection in MIMO that effectively enhanced the performance of the wireless transmission system. The major intention of this paper is to optimize the beamforming vectors and antennae by using the designed algorithm, where the spectral efficiency gets maximized. Initially, the data is collected by varying the number of users, locations, and sampling points, which is optimally selected by developing the hybrid reptile search-based lion optimization algorithm (HRS-LOA). Once it is collected, the relevant CSI is considered. This state of information is fed as input to the Auto Encoder with Bayesian Recurrent Neural Network (AE-BRNN). Finally, through the network, the optimized value of the beamforming vector and antenna is obtained, which is useful to increase the sum rate/spectral efficiency. From the numerical results, the recommended model attained a spectral efficiency of 30 bits/s/Hz, which is higher than the BWO-AE-BRNN with 13%, RSO-AE-BRNN with 16%, RSA-AE-BRNN with 17%, and LA-AE-BRNN with 40% respectively. A newly optimized beamforming vector and antenna selection using deep learning is utilized to improve the range of capacity in MIMO wireless communication models. The suggested HRS-LOA is employed to optimize the vector of beamforming from the channel state informat

关键词： auto encoder with Bayesian recurrent neural network hybrid reptile search-based lion optimization algorithm multi-input multi-output systems optimized beamforming vector and antenna selection

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