Back Propagation Model Based Machine Learning In Feed Forward Neural Networks for 6G Communication

The creation and use of 6 G technologies are anticipated to heavily use machine learning. There are a number of ways that machine learning may boost 6G, even if research and standardization are still in their early phases. Real-time network resource allocation may be made more efficient by using machine learning methods. In order to ensure effective resource utilization and enhanced network performance, this also involves dynamic spectrum allocation, power regulation, and network slicing.To improve spectrum efficiency, 6G is anticipated to make use of improved beamforming techniques and huge multiple-input, multiple-output (MIMO) devices. Beamforming pattern optimization, channel condition adaptation, and capacity enhancement can all be aided by machine learning methods. To allow wise choice-making for base station installations, interference control, and user association, machine learning may be applied to the RAN. This improves network coverage, lowers latency, and improves user experience. This paper demonstrates the training of a basic feed forward neural network using the back propagation method. The network is trained by applying the back propagation technique through the execution of the train function. After making predictions on the test set using the trained network, evaluation measures like accuracy and root mean squared error (RMSE) are calculated.