AI-Driven Detection and Estimation in Millimeter-Wave MIMO Systems

This paper focuses on problems in millimeter-wave (mmWave) MIMO systems with regards to detection and estimation errors. As mentioned before, traditional methods do not address well high-dimensional signal processing and hostile channel environments. For improving the detection and estimation the proposed method includes deep neural networks (DNNs) and deep residual network (ResNets). Our finding shows that these AI models have better performance in decreasing BER and MSE than existing techniques especially under conditions of change in SNR. Such an improvement raises the possibilities of what can be achieved through AI to handle problems that are beyond the capability as seen when compared to other methods. Specifically, the observational study also discusses critical challenges of model scalability and real-time processing while proposing further research avenues to address these concerns. In summary, the employment of the AI to the mmWave MIMO systems has the potential of expanding the horizon of communication technologies through the provision of more dependable and effective techniques. Keywords: AI-driven detection, millimeter-wave mimo, deep neural networks, channel state information, bit error rate, mean squared error