Complexity Analysis in Channel Estimation Massive MIMO Compared with LMU and GRU
MIMO: Multiple-input multiple-output technology uses multiple antennas to use reflected signals to provide channel robustness and throughput gains. It is advantageous in several applications like cellular systems, users are distributed over a wide coverage area in various applications such as mobile...
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| Autores principales: | , , |
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| Formato: | Capítulo de libro |
| Lenguaje: | inglés |
| Publicado: |
IntechOpen
2023
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| Materias: | |
| Acceso en línea: | https://www.intechopen.com/chapters/88571 |
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| Sumario: | MIMO: Multiple-input multiple-output technology uses multiple antennas to use reflected signals to provide channel robustness and throughput gains. It is advantageous in several applications like cellular systems, users are distributed over a wide coverage area in various applications such as mobile systems, improving channel state information (CSI) processing efficiency in massive MIMO systems. This chapter proposes two channel-based deep learning methods gated recurrent unit and a Legendre memory unit to enhance the performance in a massive MIMO system and compares the complexity analysis to the previous methods, The complexity analysis is based on the channel state information network combined with gated recurrent units and Legendre memory units compared to indicator parameters which show the difference between literature-based techniques. |
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| ISBN: | 978-0-85014-513-7 |
| DOI: | 10.5772/intechopen.113217 |