Transformer-based deep learning approach for channel estimation in MIMO wireless systems

In this research research, a novel channel estimation method is produced by transformers` potential for MIMO (Multiple-Input Multiple-Output) wireless systems. The demonstrated system tackles shortfalls in both the accurate and efficient channel estimation under high bit complexity and signal propagation pathways. The procedure consists of the means for learning the model how to avoid incorrect MIMO configurations by itself as the process is carried out in most of the scenarios, moreover, it is one of the scalability issues on the most part of the system environments. The results obtained in the work show that the transformer model, which not only improves the accuracy of the channel estimation but also gives the reduction of the computational complexity of the existing traditional algorithms. In some situations, conventional channel estimation is not computationally beneficial because it uses supervisor learning technology to upgrade the performance of MIMO systems. The accuracy of the channel estimation is from 80.2% to maximum 99.5% with a mean of 89.6%. The Bit Error Rate (BER) varies between 0.012 and 0.097 while the average is 5.6%, above the 0.05 threshold for nine iterations (45%). The time estimation fluctuates between 11.5 ms to 48.9 ms and it varies across the averages of 31.7 ms and the 40 ms threshold in only six periods (30%) for the potential processing delays.