A MIMO-OFDM Transmitters along with the Complex-Valued based Phases Transparency with RBF ANN

Multi-Input Multi-Output (MIMO) methods of transmission have become essential instruments in modern telecommunications for improving spectral efficiency in bandwidth-constrained contexts. Even with its effectiveness, developing affordable receivers in MIMO channels is still a difficult task. Although the maximum likelihood scanner is well known for its outstanding performance, its high computational complexity makes it difficult to use in real-world applications. We provide a revolutionary MIMO system in this creative work, using a decoding method based on the usefulness of the Spread Transmittance Radial Base Function (PTRBF) neural net. This method provides a strong substitute, demonstrating enhanced receiver efficiency while reducing the computational burden related to maximum likelihood decoding. The algorithm's practical value is considerably expanded by the resulting decrease in computing overhead. We experiment with simulations carried out under 5G wireless Lorenz channels using MIMO-Orthogonal frequencies division multiplexing (MIMO-OFDM). This option enables a thorough evaluation of performance and creates an equitable comparison with recognised reference methods. The suggested approach represents a significant breakthrough in MIMO receiver design, since it not only shows improved performance but also emphasises its practicality in real-world circumstances. Our technique is a groundbreaking addition to the rapidly changing field of wireless communication technology, as it blends efficiency with practicality.