Enhanced Performance of Signal Detection using Deep Learning Approach in Massive MIMO Systems

The demand for more bandwidth capacity is growing in response to the rise in mobile users and bandwidth-intensive services, which makes massive MIMO systems essential. This leads to a need to develop these systems further using deep learning to address the complexity of wireless channels and dynamically optimise resource allocation. However there are lots of initiatives that utilise deep learning approaches in 5G signal detection for massive MIMO, there is a research gap in comprehensive comparative analyses across multiple signal detection techniques, various MIMO sizes, and a wide range of SNR levels to provide a holistic understanding of their performance in real-world scenarios. This paper proposes a deep learning approach for enhancing the massive MIMO. A deep neural network is utilised for detecting the symbols in the massive MIMO setting, called deep MIMO. This system facilitates sequential symbol detection through an unfolded network architecture, enabling the processing of data in a layer-by-layer manner. With intensive training and optimisation, the model achieves better performance in massive MIMO scenarios.