Minimize NMSE of Massive System using Machine Learning based Channel Estimation Technique
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Abstract
In the recent years more research is going on in 5G technology. Massive MIMO (large-scale antenna systems, covering hyper MIMO) is more attractive and different from current practice in 4G technology, through the preferred use of more service antennas over active terminals and time-division, frequency division duplex operations. More number of Antennas can be used for the channel state information (CSI) estimation, which directly affects the normalized mean square error (NMSE). To provide high performance in 5G cellular networks, Massive MIMO is one of the promising methods with simple transmit and receive operations. But it is possible only with accurate channel state information at the transmitter. Because of usage of large dimensional channels is one of the challenging issues in current research. Massive Multiple-Input Multiple-Output (MIMO) systems play a crucial role in next-generation wireless communication, offering enhanced spectral and energy efficiency. However, accurate channel estimation remains a key challenge due to high-dimensionality and hardware impairments. This paper explores machine learning-based channel estimation techniques to minimize the Normalized Mean Squared Error (NMSE) in massive MIMO systems. We review traditional estimation methods, highlight the advantages of ML-based approaches, and discuss future research directions.
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