Sparse joint representation for massive MIMO satellite uplink and downlink based on dictionary learning
We address the challenge of jointly representing uplink (UL) and downlink(DL) channels for a massive multiple-input multiple-output satellite system.We employ dictionary learning for sparse representation with the goal of mini-mizing the number of UL/DL pilots and improving accuracy. Additionally, b...
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| Format: | Article |
| Language: | English |
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Electronics and Telecommunications Research Institute (ETRI)
2025-08-01
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| Series: | ETRI Journal |
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| Online Access: | https://doi.org/10.4218/etrij.2024-0190 |
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| author | Qing-Yang Guan Shuang Wu Zhuang Miao |
| author_facet | Qing-Yang Guan Shuang Wu Zhuang Miao |
| author_sort | Qing-Yang Guan |
| collection | DOAJ |
| description | We address the challenge of jointly representing uplink (UL) and downlink(DL) channels for a massive multiple-input multiple-output satellite system.We employ dictionary learning for sparse representation with the goal of mini-mizing the number of UL/DL pilots and improving accuracy. Additionally, byconsidering the angular reciprocity, a common dictionary support can beestablished to enhance the performance. However, what type of dictionarymodel is suited for UL/DL channel representation remains an unknown field.Previous research has utilized predefined dictionaries, such as DFT or ODFTbases, which are unable to adapt to dynamic scenarios. Training dictionarieshave demonstrated the potential to significantly improve accuracy; however, alack of analysis regarding dictionary constraints exists. To address this issue,we analyze the conditional constraints of the dictionary for joint UL/DLchannel representation, aiming to quantify the maximum boundary while pro-posing a constrained dictionary learning algorithm with singular value decom-position to obtain an effective representation and conduct an adaptabilityanalysis in dynamic satellite communication scenarios. |
| format | Article |
| id | doaj-art-2f41eb7c6dd0476d8f008c4dc2927d92 |
| institution | Kabale University |
| issn | 1225-6463 2233-7326 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Electronics and Telecommunications Research Institute (ETRI) |
| record_format | Article |
| series | ETRI Journal |
| spelling | doaj-art-2f41eb7c6dd0476d8f008c4dc2927d922025-08-25T06:57:15ZengElectronics and Telecommunications Research Institute (ETRI)ETRI Journal1225-64632233-73262025-08-0147461763110.4218/etrij.2024-0190Sparse joint representation for massive MIMO satellite uplink and downlink based on dictionary learningQing-Yang GuanShuang WuZhuang MiaoWe address the challenge of jointly representing uplink (UL) and downlink(DL) channels for a massive multiple-input multiple-output satellite system.We employ dictionary learning for sparse representation with the goal of mini-mizing the number of UL/DL pilots and improving accuracy. Additionally, byconsidering the angular reciprocity, a common dictionary support can beestablished to enhance the performance. However, what type of dictionarymodel is suited for UL/DL channel representation remains an unknown field.Previous research has utilized predefined dictionaries, such as DFT or ODFTbases, which are unable to adapt to dynamic scenarios. Training dictionarieshave demonstrated the potential to significantly improve accuracy; however, alack of analysis regarding dictionary constraints exists. To address this issue,we analyze the conditional constraints of the dictionary for joint UL/DLchannel representation, aiming to quantify the maximum boundary while pro-posing a constrained dictionary learning algorithm with singular value decom-position to obtain an effective representation and conduct an adaptabilityanalysis in dynamic satellite communication scenarios.https://doi.org/10.4218/etrij.2024-0190dictionary learningmassive mimosatellite channelsparse representation |
| spellingShingle | Qing-Yang Guan Shuang Wu Zhuang Miao Sparse joint representation for massive MIMO satellite uplink and downlink based on dictionary learning ETRI Journal dictionary learning massive mimo satellite channel sparse representation |
| title | Sparse joint representation for massive MIMO satellite uplink and downlink based on dictionary learning |
| title_full | Sparse joint representation for massive MIMO satellite uplink and downlink based on dictionary learning |
| title_fullStr | Sparse joint representation for massive MIMO satellite uplink and downlink based on dictionary learning |
| title_full_unstemmed | Sparse joint representation for massive MIMO satellite uplink and downlink based on dictionary learning |
| title_short | Sparse joint representation for massive MIMO satellite uplink and downlink based on dictionary learning |
| title_sort | sparse joint representation for massive mimo satellite uplink and downlink based on dictionary learning |
| topic | dictionary learning massive mimo satellite channel sparse representation |
| url | https://doi.org/10.4218/etrij.2024-0190 |
| work_keys_str_mv | AT qingyangguan sparsejointrepresentationformassivemimosatelliteuplinkanddownlinkbasedondictionarylearning AT shuangwu sparsejointrepresentationformassivemimosatelliteuplinkanddownlinkbasedondictionarylearning AT zhuangmiao sparsejointrepresentationformassivemimosatelliteuplinkanddownlinkbasedondictionarylearning |