The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Pemerolehan maklumat keadaan saluran (CSI) di bahagian pemancar merupakan cabaran utama dalam sistem MIMO besar-besaran untuk membolehkan penghantaran berkecekapan tinggi. Untuk menangani isu ini, pelbagai skim maklum balas CSI telah dicadangkan, termasuk skim maklum balas terhad dengan pengkuantitian vektor berasaskan buku kod dan maklum balas matriks saluran eksplisit. Disebabkan oleh had kapasiti saluran maklum balas, isu biasa dalam skim ini ialah perwakilan CSI yang cekap dengan bilangan bit yang terhad di bahagian penerima, dan pembinaan semula yang tepat berdasarkan bit maklum balas daripada penerima di bahagian pemancar. Baru-baru ini, diilhamkan oleh aplikasi yang berjaya dalam banyak bidang, teknologi pembelajaran mendalam (DL) untuk pemerolehan CSI telah menerima minat penyelidikan yang besar daripada kedua-dua ahli akademik dan industri. Memandangkan mekanisme maklum balas praktikal rangkaian radio (NR) baharu generasi ke-5 (5G), kami mencadangkan dua skema pelaksanaan untuk kecerdasan buatan untuk CSI (AI4CSI), penerima berasaskan DL dan reka bentuk hujung ke hujung, masing-masing. Skim AI4CSI yang dicadangkan telah dinilai dalam rangkaian 5G NR dari segi kecekapan spektrum (SE), overhed maklum balas dan kerumitan pengiraan, dan dibandingkan dengan skim warisan. Untuk menunjukkan sama ada skim ini boleh digunakan dalam senario kehidupan sebenar, kedua-dua data saluran berasaskan model dan saluran diukur secara praktikal telah digunakan dalam penyiasatan kami. Apabila pemerolehan CSI berasaskan DL digunakan pada penerima sahaja, yang mempunyai sedikit impak antara muka udara, ia memberikan kira-kira 25% keuntungan SE pada tahap overhed maklum balas yang sederhana. Ia boleh dilaksanakan dalam rangkaian 5G semasa semasa evolusi 5G. Untuk peningkatan CSI berasaskan DL hujung ke hujung, penilaian juga menunjukkan peningkatan prestasi tambahan mereka pada SE, iaitu 6%-26% berbanding dengan penerima berasaskan DL dan 33%-58% berbanding dengan skim CSI warisan. Memandangkan kesannya yang besar pada reka bentuk antara muka udara, ia akan menjadi teknologi calon untuk rangkaian generasi ke-6 (6G), di mana antara muka udara yang direka bentuk oleh kecerdasan buatan boleh digunakan.
Xin WANG
DOCOMO Beijing Communications Laboratories, Co. Ltd.
Xiaolin HOU
DOCOMO Beijing Communications Laboratories, Co. Ltd.
Lan CHEN
DOCOMO Beijing Communications Laboratories, Co. Ltd.
Yoshihisa KISHIYAMA
NTT DOCOMO, INC.
Takahiro ASAI
NTT DOCOMO, INC.
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Salinan
Xin WANG, Xiaolin HOU, Lan CHEN, Yoshihisa KISHIYAMA, Takahiro ASAI, "Deep Learning-Based Massive MIMO CSI Acquisition for 5G Evolution and 6G" in IEICE TRANSACTIONS on Communications,
vol. E105-B, no. 12, pp. 1559-1568, December 2022, doi: 10.1587/transcom.2022EBP3009.
Abstract: Channel state information (CSI) acquisition at the transmitter side is a major challenge in massive MIMO systems for enabling high-efficiency transmissions. To address this issue, various CSI feedback schemes have been proposed, including limited feedback schemes with codebook-based vector quantization and explicit channel matrix feedback. Owing to the limitations of feedback channel capacity, a common issue in these schemes is the efficient representation of the CSI with a limited number of bits at the receiver side, and its accurate reconstruction based on the feedback bits from the receiver at the transmitter side. Recently, inspired by successful applications in many fields, deep learning (DL) technologies for CSI acquisition have received considerable research interest from both academia and industry. Considering the practical feedback mechanism of 5th generation (5G) New radio (NR) networks, we propose two implementation schemes for artificial intelligence for CSI (AI4CSI), the DL-based receiver and end-to-end design, respectively. The proposed AI4CSI schemes were evaluated in 5G NR networks in terms of spectrum efficiency (SE), feedback overhead, and computational complexity, and compared with legacy schemes. To demonstrate whether these schemes can be used in real-life scenarios, both the modeled-based channel data and practically measured channels were used in our investigations. When DL-based CSI acquisition is applied to the receiver only, which has little air interface impact, it provides approximately 25% SE gain at a moderate feedback overhead level. It is feasible to deploy it in current 5G networks during 5G evolutions. For the end-to-end DL-based CSI enhancements, the evaluations also demonstrated their additional performance gain on SE, which is 6%-26% compared with DL-based receivers and 33%-58% compared with legacy CSI schemes. Considering its large impact on air-interface design, it will be a candidate technology for 6th generation (6G) networks, in which an air interface designed by artificial intelligence can be used.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2022EBP3009/_p
Salinan
@ARTICLE{e105-b_12_1559,
author={Xin WANG, Xiaolin HOU, Lan CHEN, Yoshihisa KISHIYAMA, Takahiro ASAI, },
journal={IEICE TRANSACTIONS on Communications},
title={Deep Learning-Based Massive MIMO CSI Acquisition for 5G Evolution and 6G},
year={2022},
volume={E105-B},
number={12},
pages={1559-1568},
abstract={Channel state information (CSI) acquisition at the transmitter side is a major challenge in massive MIMO systems for enabling high-efficiency transmissions. To address this issue, various CSI feedback schemes have been proposed, including limited feedback schemes with codebook-based vector quantization and explicit channel matrix feedback. Owing to the limitations of feedback channel capacity, a common issue in these schemes is the efficient representation of the CSI with a limited number of bits at the receiver side, and its accurate reconstruction based on the feedback bits from the receiver at the transmitter side. Recently, inspired by successful applications in many fields, deep learning (DL) technologies for CSI acquisition have received considerable research interest from both academia and industry. Considering the practical feedback mechanism of 5th generation (5G) New radio (NR) networks, we propose two implementation schemes for artificial intelligence for CSI (AI4CSI), the DL-based receiver and end-to-end design, respectively. The proposed AI4CSI schemes were evaluated in 5G NR networks in terms of spectrum efficiency (SE), feedback overhead, and computational complexity, and compared with legacy schemes. To demonstrate whether these schemes can be used in real-life scenarios, both the modeled-based channel data and practically measured channels were used in our investigations. When DL-based CSI acquisition is applied to the receiver only, which has little air interface impact, it provides approximately 25% SE gain at a moderate feedback overhead level. It is feasible to deploy it in current 5G networks during 5G evolutions. For the end-to-end DL-based CSI enhancements, the evaluations also demonstrated their additional performance gain on SE, which is 6%-26% compared with DL-based receivers and 33%-58% compared with legacy CSI schemes. Considering its large impact on air-interface design, it will be a candidate technology for 6th generation (6G) networks, in which an air interface designed by artificial intelligence can be used.},
keywords={},
doi={10.1587/transcom.2022EBP3009},
ISSN={1745-1345},
month={December},}
Salinan
TY - JOUR
TI - Deep Learning-Based Massive MIMO CSI Acquisition for 5G Evolution and 6G
T2 - IEICE TRANSACTIONS on Communications
SP - 1559
EP - 1568
AU - Xin WANG
AU - Xiaolin HOU
AU - Lan CHEN
AU - Yoshihisa KISHIYAMA
AU - Takahiro ASAI
PY - 2022
DO - 10.1587/transcom.2022EBP3009
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E105-B
IS - 12
JA - IEICE TRANSACTIONS on Communications
Y1 - December 2022
AB - Channel state information (CSI) acquisition at the transmitter side is a major challenge in massive MIMO systems for enabling high-efficiency transmissions. To address this issue, various CSI feedback schemes have been proposed, including limited feedback schemes with codebook-based vector quantization and explicit channel matrix feedback. Owing to the limitations of feedback channel capacity, a common issue in these schemes is the efficient representation of the CSI with a limited number of bits at the receiver side, and its accurate reconstruction based on the feedback bits from the receiver at the transmitter side. Recently, inspired by successful applications in many fields, deep learning (DL) technologies for CSI acquisition have received considerable research interest from both academia and industry. Considering the practical feedback mechanism of 5th generation (5G) New radio (NR) networks, we propose two implementation schemes for artificial intelligence for CSI (AI4CSI), the DL-based receiver and end-to-end design, respectively. The proposed AI4CSI schemes were evaluated in 5G NR networks in terms of spectrum efficiency (SE), feedback overhead, and computational complexity, and compared with legacy schemes. To demonstrate whether these schemes can be used in real-life scenarios, both the modeled-based channel data and practically measured channels were used in our investigations. When DL-based CSI acquisition is applied to the receiver only, which has little air interface impact, it provides approximately 25% SE gain at a moderate feedback overhead level. It is feasible to deploy it in current 5G networks during 5G evolutions. For the end-to-end DL-based CSI enhancements, the evaluations also demonstrated their additional performance gain on SE, which is 6%-26% compared with DL-based receivers and 33%-58% compared with legacy CSI schemes. Considering its large impact on air-interface design, it will be a candidate technology for 6th generation (6G) networks, in which an air interface designed by artificial intelligence can be used.
ER -