1. Channel estimation for massive MIMO system using the shannon entropy function.
- Author
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Albataineh, Zaid, Al-Zoubi, Nebal, and Musa, Ahmed
- Subjects
- *
UNCERTAINTY (Information theory) , *CHANNEL estimation , *MIMO systems , *ANTENNAS (Electronics) , *PATTERNMAKING , *NUMBER systems - Abstract
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 information (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 performance 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 for 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 outperforms existing compressive sensing (CS)-based channel estimation techniques in terms of NMSE performance and effectively lowers pilot overhead. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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