CVNN-based Channel Estimation and Equalization in OFDM Systems Without Cyclic Prefix
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866929462215442432 |
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| author | Sousa, Heitor dos Santos Soares, Jonathan Aguiar Mayer, Kayol Soares Arantes, Dalton Soares |
| author_facet | Sousa, Heitor dos Santos Soares, Jonathan Aguiar Mayer, Kayol Soares Arantes, Dalton Soares |
| contents | In modern communication systems operating with Orthogonal Frequency-Division Multiplexing (OFDM), channel estimation requires minimal complexity with one-tap equalizers. However, this depends on cyclic prefixes, which must be sufficiently large to cover the channel impulse response. Conversely, the use of cyclic prefix (CP) decreases the useful information that can be conveyed in an OFDM frame, thereby degrading the spectral efficiency of the system. In this context, we study the impact of CPs on channel estimation with complex-valued neural networks (CVNNs). We show that the phase-transmittance radial basis function neural network offers superior results, in terms of required energy per bit, compared to classical minimum mean-squared error and least squares algorithms in scenarios without CP. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_13623 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | CVNN-based Channel Estimation and Equalization in OFDM Systems Without Cyclic Prefix Sousa, Heitor dos Santos Soares, Jonathan Aguiar Mayer, Kayol Soares Arantes, Dalton Soares Information Theory Signal Processing In modern communication systems operating with Orthogonal Frequency-Division Multiplexing (OFDM), channel estimation requires minimal complexity with one-tap equalizers. However, this depends on cyclic prefixes, which must be sufficiently large to cover the channel impulse response. Conversely, the use of cyclic prefix (CP) decreases the useful information that can be conveyed in an OFDM frame, thereby degrading the spectral efficiency of the system. In this context, we study the impact of CPs on channel estimation with complex-valued neural networks (CVNNs). We show that the phase-transmittance radial basis function neural network offers superior results, in terms of required energy per bit, compared to classical minimum mean-squared error and least squares algorithms in scenarios without CP. |
| title | CVNN-based Channel Estimation and Equalization in OFDM Systems Without Cyclic Prefix |
| topic | Information Theory Signal Processing |
| url | https://arxiv.org/abs/2308.13623 |