Optimization of channel estimation for millimeter-wave massive MIMO networks

Abstract

The 5th generation of cellular communication is a highly competitive market that promises some distinct features compared to the legacy LTE (long-term evolution) era. Some of these exclusive features include enhanced mobile broadband (eMBB), massive machine type communications (mMTC), and ultra-reliable low latency communication (URLLC) traffic (1 ms one-way latency, 99.999% reliability). Enabling these cuttingedge technologies requires a very smooth processing of user data and transmitted signals. Channel estimation and acquisition of channel state data is one of the important points in this regard because they can eventually enable signal transmission and subsequent processing. However, most of the estimators in research nowadays suffer from high complexity due to either too many constraints or conditions for unique solutions. This is already a significant problem in the communication industry because of its high dependency on resource allocation and system overhead. This research focuses on the enhancement of the legacy channel estimation processes to fit the 5G cellular standards. The industry standard Least Squares (LS) estimator was used as the basis for the optimized estimation. A dual residual function was enabled instead of a single one to make the estimator adaptive. Results show that making the weight function adaptive reduces the error at the receiver and provides a sharper response curve. A comprehensive study was carried out against the trending compressed sensing (CS) based semi-blind estimators as a second objective. And finally, the optimized algorithm was characterized on MIMO-OFDM systems to show its performance improvements in the cases of large arrays. These objectives were carried out through simulation, and results were constructively discussed based on the earlier points. Results were compared with parameters SNR, SER, PER, and BER. Some potentials regarding the channel estimation in MIMO-OFDM were left as pick-up points for future research interests.