A master's thesis by researcher Sura Naseh Salman was defended at the University of Basrah’s College of Engineering (Department of Computer Engineering), under the supervision of Dr. Muhannad Hamid Khalaf, titled: Machine Learning for Channel Estimation in One-Bit Massive MIMO Networks

This thesis investigates the problem of channel estimation in the uplink direction of massive
MIMO systems with ADCs of one-bit resolution. It uses the concept of AdaBoost for channel
estimation and proposes three estimators, namely, Ada-BLMMSE, Ada-BLS, and Ada-SVM. The
thesis also generalizes the regular AdaBoost through letting AdaBoost to depend on multiple basis
estimators rather than a single basis estimator per boosting iteration. Capitalized on the generalized
AdaBoost, the thesis derives two additional algorithms for channel estimation, and they are termed
sequential AdaBoost (Ada-Seq) and parallel AdaBoost (Ada-Par). Numerical simulations indicate
that performance of Ada-Seq and Ada-Par can outperform that of Ada-BLMMSE, Ada-BLS, Ada-
SVM, and other benchmarks as training length or signal-to-noise ratio goes high. Also, the results
show that Ada-Seq and Ada-Par hold adequate complexity and consume reasonable running times
when implementation