Optimized Neuro-Adaptive Twin Pulse-Coupled Estimators for Efficient Channel Estimation in Heterogeneous 5G MIMO-OFDM Communication Systems

Abstract

Optimal performance in 5G and beyond MIMO-OFDM systems is achieved by channel estimation, which is crucial due to the enormous hurdles posed by dynamic and frequency-selective channel circumstances. Advanced methods of neural networks and optimization are gradually being applied in order to solve these difficulties. The heterogeneous nature of 5G-and-beyond networks introduces severe multipath fading, high mobility, and interference, complicating accurate Channel State Information (CSI) estimation. Existing techniques are sometimes difficult to compute efficiently while at the same time providing a precise estimation of interference in such scenarios. This research develops an Optimized Neuro-Adaptive Twin Pulse-Coupled Estimators for Efficient Channel Estimation in Heterogeneous 5G-and-Beyond MIMO-OFDM Communication Systems (STEB-Twin-APCNet) to improve the channel estimation by integrating Twin Adaptive Pulse Coupled Network with Sooty Tern Evolutionary Boost optimization. The objective of this study is to design and optimize a neuro-adaptive channel estimator capable of real-time CSI acquisition with high accuracy and minimal complexity in diverse 5G environments. To test the model in different channel scenarios, MATLAB simulations were run with the help of deep learning and 5G toolboxes. The results show that the suggested STEB-Twin-APCNet outperforms the standard approaches with a channel estimate accuracy of over 99.8%, dependability of 99.5% in high-mobility situations, and a decrease of 99.3% in estimation error. These measures demonstrate how efficient and resilient the system is. As a result, channel prediction for next-gen wireless networks is made easier using this adaptive framework.