Variational autoencoder-enhanced deep neural network-based detection for MIMO systems
Lately, there has been a substantial surge of interest in artificial intelligence (AI) as a promising technology to tremendously elevate the efficiency of multiple-input multiple-output (MIMO) detection within wireless communication networks. AI-aided methodologies like deep neural networks (DNNs) h...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | e-Prime: Advances in Electrical Engineering, Electronics and Energy |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772671123002309 |
| _version_ | 1797388637665492992 |
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| author | Gevira Omondi Thomas O. Olwal |
| author_facet | Gevira Omondi Thomas O. Olwal |
| author_sort | Gevira Omondi |
| collection | DOAJ |
| description | Lately, there has been a substantial surge of interest in artificial intelligence (AI) as a promising technology to tremendously elevate the efficiency of multiple-input multiple-output (MIMO) detection within wireless communication networks. AI-aided methodologies like deep neural networks (DNNs) have empowered MIMO receivers to understand intricate channel dynamics and interference contexts, ultimately leading to noteworthy enhancements in throughput and the performance of error rates. However, the techniques in existing literature face challenges in effectively reconciling accuracy and efficiency across a spectrum of channel conditions. Therefore, this study presents a state-of-the-art DNN-centric architecture for MIMO signal detection called variational autoencoder-enhanced DNN-based detection (VAE-DNN-Det), which harnesses the power of variational autoencoders (VAEs) to efficiently capture underlying data distributions, thereby enhancing the DNN’s ability to adapt to complex channel scenarios and achieve near-optimal performance. Simulations are carried out to contrast the bit error rate (BER) performance of the proposed scheme with traditional methods, where the proposed VAE-DNN-Det method attains a signal-to-noise ratio gain of nearly 1 dB compared to the traditional detection methods. |
| first_indexed | 2024-03-08T22:43:41Z |
| format | Article |
| id | doaj.art-d24b300963bf4264b490227dac7cee9d |
| institution | Directory Open Access Journal |
| issn | 2772-6711 |
| language | English |
| last_indexed | 2024-03-08T22:43:41Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | e-Prime: Advances in Electrical Engineering, Electronics and Energy |
| spelling | doaj.art-d24b300963bf4264b490227dac7cee9d2023-12-17T06:43:27ZengElseviere-Prime: Advances in Electrical Engineering, Electronics and Energy2772-67112023-12-016100335Variational autoencoder-enhanced deep neural network-based detection for MIMO systemsGevira Omondi0Thomas O. Olwal1Corresponding author at: School of Engineering, University of Nairobi, Nairobi, Kenya.; Department of Electrical Engineering/F’SATI, Tshwane University of Technology, Pretoria, South Africa; School of Engineering, University of Nairobi, Nairobi, KenyaDepartment of Electrical Engineering/F’SATI, Tshwane University of Technology, Pretoria, South AfricaLately, there has been a substantial surge of interest in artificial intelligence (AI) as a promising technology to tremendously elevate the efficiency of multiple-input multiple-output (MIMO) detection within wireless communication networks. AI-aided methodologies like deep neural networks (DNNs) have empowered MIMO receivers to understand intricate channel dynamics and interference contexts, ultimately leading to noteworthy enhancements in throughput and the performance of error rates. However, the techniques in existing literature face challenges in effectively reconciling accuracy and efficiency across a spectrum of channel conditions. Therefore, this study presents a state-of-the-art DNN-centric architecture for MIMO signal detection called variational autoencoder-enhanced DNN-based detection (VAE-DNN-Det), which harnesses the power of variational autoencoders (VAEs) to efficiently capture underlying data distributions, thereby enhancing the DNN’s ability to adapt to complex channel scenarios and achieve near-optimal performance. Simulations are carried out to contrast the bit error rate (BER) performance of the proposed scheme with traditional methods, where the proposed VAE-DNN-Det method attains a signal-to-noise ratio gain of nearly 1 dB compared to the traditional detection methods.http://www.sciencedirect.com/science/article/pii/S2772671123002309Artificial intelligenceMIMO detectionEmerging wireless communicationDeep neural networkDeep learningVariational autoencoder |
| spellingShingle | Gevira Omondi Thomas O. Olwal Variational autoencoder-enhanced deep neural network-based detection for MIMO systems e-Prime: Advances in Electrical Engineering, Electronics and Energy Artificial intelligence MIMO detection Emerging wireless communication Deep neural network Deep learning Variational autoencoder |
| title | Variational autoencoder-enhanced deep neural network-based detection for MIMO systems |
| title_full | Variational autoencoder-enhanced deep neural network-based detection for MIMO systems |
| title_fullStr | Variational autoencoder-enhanced deep neural network-based detection for MIMO systems |
| title_full_unstemmed | Variational autoencoder-enhanced deep neural network-based detection for MIMO systems |
| title_short | Variational autoencoder-enhanced deep neural network-based detection for MIMO systems |
| title_sort | variational autoencoder enhanced deep neural network based detection for mimo systems |
| topic | Artificial intelligence MIMO detection Emerging wireless communication Deep neural network Deep learning Variational autoencoder |
| url | http://www.sciencedirect.com/science/article/pii/S2772671123002309 |
| work_keys_str_mv | AT geviraomondi variationalautoencoderenhanceddeepneuralnetworkbaseddetectionformimosystems AT thomasoolwal variationalautoencoderenhanceddeepneuralnetworkbaseddetectionformimosystems |