Efficient Enhanced K-Means Clustering for Semi-Blind Channel Estimation of Cell-Free Massive MIMO

We propose an efficient enhanced K-means clustering (E-KMC) algorithm for semi-blind channel estimation of uplink cell-free massive multiple-input multiple-output (MIMO) systems in factory automation, an important application of the internet of things (IoT). The proposed E-KMC algorithm operates with significantly less clusters and complexity than the KMC algorithm while achieving enhanced bit error rate (BER) performance, as the latter converges extremely slowly even with just medium modulation order and a medium number of transmit antennas. A near-optimal short pilot is designed to assist clustering of the E-KMC based channel estimation scheme. The semi-blind receiver structure achieves a BER performance that is very close to the case with perfect channel state information (CSI), as well as a mean square error (MSE) of channel estimation that is very close to the theoretical lower bound derived in the paper. The proposed E-KMC based channel estimation scheme also significantly outperforms other types of semi-blind channel estimation approaches including second- and higher-order statistics based and machine learning based approaches, while at a much lower complexity. In addition, the E-KMC based channel estimation, is conducted at central processing unit (CPU) and avoids excessive fronthaul overhead due to exchange of the estimated CSI between access points (APs) and CPU.