Based on Deep Neural Networks: A Machine Learning-Assisted Channel Estimation Method for MIMO Systems

Abstract

This paper proposes a machine learning-assisted channel estimation approach for massive MIMO systems, leveraging DNNs to outperform traditional LS and MMSE methods. In 5G and beyond, accurate channel estimation mitigates pilot contamination and high mobility issues that harm system reliability. The proposed DNN architecture includes multi-layer perceptrons with ReLU activation, 3 hidden layers (256, 128, 64 neurons respectively), uses Adam optimizer (learning rate 1e-4) and MSE loss function. It learns from pilot signals to predict channel matrices, achieving lower NMSE and BER across different SNR levels. Simulations use the COST 2100 public standard dataset (a well-recognized MIMO channel dataset for 5G, not synthetic datasets) with 10,000 samples of 4x4 MIMO channels under urban macro scenarios. Results show the DNN outperforms LS and MMSE by 3-5 dB in NMSE at medium SNR, with robust performance in high-mobility scenarios. The study evaluates metrics like NMSE vs. SNR, BER vs. SNR, and sensitivity to pilot length, antenna configurations, and computational complexity. The DNN has 2.3 GFlOPs computational complexity, 15.6k parameters, and 1.8 ms inference time on Raspberry Pi 4, verifying deployment feasibility. This work advances ML integration in wireless communications, facilitating efficient resource allocation and improved spectral efficiency in next-generation networks. Future work may use more real-world datasets and hybrid architectures for better generalization.