Data driven 3D channel estimation for massive MIMO

摘要

Massive MIMO is considered as promising technology enabling 5G and beyond 5G cellular communication networks. High-resolution gain and angle estimation of the channel are significant challenges in the design of massive MIMO. The existing signal subspace-based estimation techniques lack the resolution for the detection of slight angles. The severity increases when both azimuth and elevation angle are jointly estimating for the 3D channel. The existing techniques rely on the combination of signal subspace-based approaches to estimate both azimuth and elevation angles. The resulting estimate’s accuracy is less due to its low resolution and angular coupling. Data-driven techniques can address this issue. This work is the first-ever attempt to apply data-driven techniques for 3D channel estimation of massive MIMO to the best of our knowledge. This paper considers two approaches for the same, one based on K nearest neighbour (KNN) and the other based on deep neural network (DNN) with restricted Boltzmann machine (RBM). We also investigated data generation and feature extraction for data-driven communication technologies in three ways. An intensive performance analysis of both architectures using these three feature vectors is carried out. The simulation reveals deep learning (DL) model’s superiority compared to the machine learning (ML)-based counterpart and other signal subspace-based estimation techniques. SNR of 10 dB lesser than other signal subspace estimation is required for ML-based approach, whereas the DL-based estimator needs 20 dB lesser SNR for the received signal to achieve the same BER value. In the comparative study on the feature vectors for data-driven estimation techniques, the data processing based on the Pearson correlation feature vector (PCFV) performs the best. Performance comparison of the DNN model with the KNN model is based on tenfold cross-validation showing an average AUC of 0.915 for DL based estimation and a coefficient of 0.904 for ML-based counterpart.