The advent of millimeter-wave (mmWave) massive Multiple Input Multiple Output (MIMO) technology has introduced unprecedented challenges and opportunities in wireless communication systems. The effective estimation of mmWave channels plays a pivotal role in ensuring reliable and high-capacity data transmission. This thesis addresses the critical task of beamspace channel estimation in mmWave massive MIMO systems using a hybrid approach that combines Kalman filtering, subspace pursuit, and orthogonal matching pursuit (OMP) techniques.

The mmWave frequency band offers a wide spectrum for high data rates, but it is characterized by severe path loss and susceptibility to blockage due to its short wavelength. To harness the potential of mmWave communication, beamforming and beamsteering techniques are employed, necessitating accurate beamspace channel estimation.

The proposed hybrid approach leverages the strengths of Kalman filtering, a recursive estimation algorithm, to provide dynamic tracking of channel variations. By incorporating Kalman filtering into the estimation process, the system adapts to changes in the environment, offering robustness in time-varying scenarios.