<![CDATA[Channel estimation is a significant challenge in 5G NR and future communication systems because of complicated propagation settings, including high-order modulation and Nakagami-m fading. High spectral efficiency is necessary to satisfy increasing data demands. To improve channel prediction in a 32×32 Massive MIMO architecture, this study suggests a unified framework that combines Deep Neural Networks (DNN), compressed pilot signals, and Spectrally Efficient Frequency Division Multiplexing (SEFDM). The system utilizes SNR as an input characteristic for the deep learning model and 256-QAM modulation. With average MSE (Mean Square Error) values of 1.2776 for LSE (Lest Square Estimation ) and 1.055 for MMSE (Minimum Mean Square Error ), simulation findings show that traditional estimators like LSE and MMSE perform well in moderate-SNR settings. The average MSE of 0.498 obtained by the DNN-based estimator is much lower and best. The model's advantage in capturing nonlinear channel features is shown by graphical comparisons of real vs. anticipated channel gains, leading to better reliability and throughput. In summary, the DNN model exhibits exceptional performance and versatility for real-time channel prediction in spectrally efficient next-generation communication systems, e.g., IoT, autonomous systems.]]>
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