Optimal multiuser uplink data detection for 5G communication networks

The complex channel fading statistics and large antenna array employed in massive multiple input and multiple outputs (MIMO) for uplink and downlink transmission. The large channel array makes it challenging to recognize the most accurate data from a composite signal at the receiver. This is one of the crucial problems that needs to be solved in 5G and next-generation communication. In order to address this tricky problem in contemporary wireless systems, a new investigation is being done on the development of optimal detectors employing bio-inspired algorithms. With bio-inspired evolutionary algorithms, an effective uplink data detection model is put forth in this article. For MIMO uplink transmission, the performance of data detectors is compared using numerical techniques, compressed sensing, state space adaptive filtering, and bio-inspired algorithms. The analysis using bio-inspired evolutionary algorithms, including Reptile Search algorithm (RSA), Dandelion optimization (DO), Harris Hawks optimization (HHO), Runge Kutta optimization (RUN), Zebra optimization algorithm (ZOA), and Beluga whale optimization (BWO), provides a clear picture for choosing the best detector for modern wireless applications. Performance metrics like bit error rate (BER) and mean square error (MSE) are used to analyze and compare the simulation outcomes. It has been determined after careful investigation that RSA-based data detectors work better than other alternatives and can be effectively used by wireless receivers to retrieve signals reliably. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.