Channel Estimation in Millimeter-Wave Massive MIMO Systems using Kalman Filter and Orthogonal Matching Pursuit
Volume: 12 - Issue: 11 - Date: 01-11-2023
Approved ISSN: 2278-1412
Published Id: IJAECESTU166 | Page No.: 36-41
Author: Sandeep Sarwan
Co- Author: Prof. Amit Pandey
Abstract:-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.
Key Words:-Millimeter-Wave (Mmwave), Massive MIMO, Beamspace Channel Estimation, Approximate Message Passing (AMP), Deep Learning.
Area:-Engineering
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