Uniform Quantization for Multi-Antenna Amplify–Quantize–Forward Relay
In this paper, we investigate a multi-antenna amplify-quantize–forward (AQF) relay system, where a half-duplex relay quantizes both the real and imaginary components of amplified received signals, storing them in memory before forwarding them to a destination. Our focus lies on uniform qu...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10749756/ |
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| author | Gangsan Jeong Xianglan Jin |
| author_facet | Gangsan Jeong Xianglan Jin |
| author_sort | Gangsan Jeong |
| collection | DOAJ |
| description | In this paper, we investigate a multi-antenna amplify-quantize–forward (AQF) relay system, where a half-duplex relay quantizes both the real and imaginary components of amplified received signals, storing them in memory before forwarding them to a destination. Our focus lies on uniform quantization at the relay in the multiple-input multiple-output (MIMO) AQF relay channel. We start with theoretical analyses to determine quantization step sizes by deriving the power and mean square error of the quantized signals as functions of these step sizes. Subsequently, we introduce neural network-based deep learning methods to mitigate computational complexity. Specifically, we present supervised learning (SL) and unsupervised learning (USL) methodologies, with the latter employing a novel loss function designed to avoid the need for extensive training data collection. Furthermore, we propose a USL method with Monte Carlo (USL-MC), leveraging an approximated loss function to address the heightened complexity observed in multi-antenna systems. Given the challenge of finding optimal step sizes for the MIMO AQF relay, comparing outcomes across different determination algorithms proves to be a significant obstacle. To address this, we evaluate error performance at the destination for the entire AQF relay communication system by introducing a linear detection method with significantly reduced complexity in the MIMO AQF relay channel. The numerical results demonstrate that the quantization step sizes determined through deep learning methods exhibit excellent performance with small computational burden during implementation, particularly when compared to the theoretical approach. Notably, the USL-MC method stands out for its significantly reduced complexity, both in practical implementation and training, rendering half-duplex AQF relays viable for practical resource-constrained systems. |
| format | Article |
| id | doaj-art-2e1c8fc1d1354505bc932667f82e1974 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| spelling | doaj-art-2e1c8fc1d1354505bc932667f82e19742025-01-07T00:02:11ZengIEEEIEEE Access2169-35362025-01-01132127214010.1109/ACCESS.2024.349475210749756Uniform Quantization for Multi-Antenna Amplify–Quantize–Forward RelayGangsan Jeong0https://orcid.org/0009-0009-4121-3778Xianglan Jin1https://orcid.org/0000-0002-6131-2556Division of Electronic Engineering, IT Convergence Research Center, Jeonbuk National University, Jeonju-si, South KoreaDivision of Electronic Engineering, IT Convergence Research Center, Jeonbuk National University, Jeonju-si, South KoreaIn this paper, we investigate a multi-antenna amplify-quantize–forward (AQF) relay system, where a half-duplex relay quantizes both the real and imaginary components of amplified received signals, storing them in memory before forwarding them to a destination. Our focus lies on uniform quantization at the relay in the multiple-input multiple-output (MIMO) AQF relay channel. We start with theoretical analyses to determine quantization step sizes by deriving the power and mean square error of the quantized signals as functions of these step sizes. Subsequently, we introduce neural network-based deep learning methods to mitigate computational complexity. Specifically, we present supervised learning (SL) and unsupervised learning (USL) methodologies, with the latter employing a novel loss function designed to avoid the need for extensive training data collection. Furthermore, we propose a USL method with Monte Carlo (USL-MC), leveraging an approximated loss function to address the heightened complexity observed in multi-antenna systems. Given the challenge of finding optimal step sizes for the MIMO AQF relay, comparing outcomes across different determination algorithms proves to be a significant obstacle. To address this, we evaluate error performance at the destination for the entire AQF relay communication system by introducing a linear detection method with significantly reduced complexity in the MIMO AQF relay channel. The numerical results demonstrate that the quantization step sizes determined through deep learning methods exhibit excellent performance with small computational burden during implementation, particularly when compared to the theoretical approach. Notably, the USL-MC method stands out for its significantly reduced complexity, both in practical implementation and training, rendering half-duplex AQF relays viable for practical resource-constrained systems.https://ieeexplore.ieee.org/document/10749756/Detectionmultiple-input multiple-output (MIMO)neural networkquantizerelay |
| spellingShingle | Gangsan Jeong Xianglan Jin Uniform Quantization for Multi-Antenna Amplify–Quantize–Forward Relay IEEE Access Detection multiple-input multiple-output (MIMO) neural network quantize relay |
| title | Uniform Quantization for Multi-Antenna Amplify–Quantize–Forward Relay |
| title_full | Uniform Quantization for Multi-Antenna Amplify–Quantize–Forward Relay |
| title_fullStr | Uniform Quantization for Multi-Antenna Amplify–Quantize–Forward Relay |
| title_full_unstemmed | Uniform Quantization for Multi-Antenna Amplify–Quantize–Forward Relay |
| title_short | Uniform Quantization for Multi-Antenna Amplify–Quantize–Forward Relay |
| title_sort | uniform quantization for multi antenna amplify x2013 quantize x2013 forward relay |
| topic | Detection multiple-input multiple-output (MIMO) neural network quantize relay |
| url | https://ieeexplore.ieee.org/document/10749756/ |
| work_keys_str_mv | AT gangsanjeong uniformquantizationformultiantennaamplifyx2013quantizex2013forwardrelay AT xianglanjin uniformquantizationformultiantennaamplifyx2013quantizex2013forwardrelay |