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Channel estimation for massive MIMO system using the shannon entropy function
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DOI：10.1007/s10586-022-03783-0.png)
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Massive MIMO systems with a large number of antennas at the base station (BS) may significantly boost spectrum and energy efficiency. In massive MIMO systems, it's important to have accurate channel state in;
mation (CSI) to get the most out of the large number of antennas and make sure the system works well. But because there are so many antennas at the base station (BS), the massive MIMO system has a lot of pilot overhead, which hurts system per;
mance a lot. The spatial correlations between the signal sources in MIMO systems are low. This pattern of distribution makes it possible to use compressive sensing in massive MIMO systems to solve the channel estimation problem. In this study, we used the Shannon entropy function to come up with a new way to estimate the channel in the downlink of an FDD massive MIMO system. The Shannon entropy function is used as a sparsity regularizer;
downlink channel estimation in the presented method to reduce the amount of work done by the pilot. The simulation results show that the proposed system outper;
ms existing compressive sensing (CS)-based channel estimation techniques in terms of NMSE per;
mance and effectively lowers pilot overhead.
Keyword:
Wireless networks and communication
Massive MIMO
Shannon entropy function (SEF)
Channel estimation
Compressive sensing (CS)
Normalized mean square error (NMSE)
Frequency division duplexing (FDD)
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4.1
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