[논문]Multi-User Hybrid Beamforming System Based on Deep Neural Network in Millimeter-Wave Communication

Sung, Lakju; Cho, Dong-Ho

doi:10.1109/access.2020.2990317

[해외논문] Multi-User Hybrid Beamforming System Based on Deep Neural Network in Millimeter-Wave Communication 원문보기

IEEE access : practical research, open solutions, v.8, 2020년, pp.91616 - 91623

Sung, Lakju (Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea) , Cho, Dong-Ho (Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea)

Abstract ▼ AI-Helper

Millimeter-wave (mmWave) communication with a large bandwidth can result in a significantly improved data rate in wireless communications. To overcome high path-loss in the mmWave frequency band, beamforming technology is necessary. Especially, there has been widespread interest in development of hybrid beamforming (HB) technologies, in view of reducing cost and power consumption in massive multiple input multiple output (MIMO) systems. Some of existing researches on HB algorithms assumed perfect channel state information (CSI) and the others used beam training process in case of assuming imperfect CSI. When beam training process is used, enough beam training has to be conducted to achieve sufficient system performance in massive MIMO systems, which results in significant training overhead. Thus, it is necessary to reduce beam training complexity. Compared to state-of-the-art technology, we propose a multi-user HB system using codebooks based on a deep neural network (DNN) in this paper. In our proposed scheme, beam codewords for the base station (BS) and all users can be inferred using limited beam training in cases when the channel state information (CSI) is unknown. In order to apply the proposed scheme to situations where the CSI is unknown, reference radio frequency (RF) beamformers were introduced. Also, the proposed DNN structure is designed considering introduced reference RF beamformers. By using the proposed DNN with reference RF beamformers, the proposed system can inferred optimal beam codewords with limited beam training. Results obtained from simulations indicate that the proposed scheme can achieve almost the same performance as a conventional scheme with less beam training complexity. We also show that the performances achieved by the proposed scheme are gradually increased as training epoch is increased, eventually converging to a steady-state value.

참고문헌 (25)

Kim, Minhoe, Kim, Nam-I, Lee, Woongsup, Cho, Dong-Ho. Deep Learning-Aided SCMA. IEEE communications letters : a publication of the IEEE Communications Society, vol.22, no.4, 720-723.

상세보기
Lee, Woongsup, Kim, Minhoe, Cho, Dong-Ho. Deep Power Control: Transmit Power Control Scheme Based on Convolutional Neural Network. IEEE communications letters : a publication of the IEEE Communications Society, vol.22, no.6, 1276-1279.

상세보기
Alkhateeb, Ahmed, Alex, Sam, Varkey, Paul, Li, Ying, Qu, Qi, Tujkovic, Djordje. Deep Learning Coordinated Beamforming for Highly-Mobile Millimeter Wave Systems. IEEE access : practical research, open solutions, vol.6, 37328-37348.

상세보기
Long, Yin, Chen, Zhi, Fang, Jun, Tellambura, Chintha. Data-Driven-Based Analog Beam Selection for Hybrid Beamforming Under mm-Wave Channels. IEEE journal of selected topics in signal processing, vol.12, no.2, 340-352.

상세보기
Huang, Hongji, Song, Yiwei, Yang, Jie, Gui, Guan, Adachi, Fumiyuki. Deep-Learning-Based Millimeter-Wave Massive MIMO for Hybrid Precoding. IEEE transactions on vehicular technology, vol.68, no.3, 3027-3032.

상세보기
5G Study on channel model for frequencies from 0 5 to 100 GHz 2017
10.1109/GLOCOM.2014.7037315
Taga, T.. Analysis for mean effective gain of mobile antennas in land mobile radio environments. IEEE transactions on vehicular technology, vol.39, no.2, 117-131.

상세보기
Song Noh, Zoltowski, Michael D., Love, David J.. Training Sequence Design for Feedback Assisted Hybrid Beamforming in Massive MIMO Systems. IEEE transactions on communications, vol.64, no.1, 187-200.

상세보기
arXiv 1502 03167 Batch normalization: Accelerating deep network training by reducing internal covariate shift ioffe 2015
Lu Lu, Li, Geoffrey Ye, Swindlehurst, A. Lee, Ashikhmin, Alexei, Rui Zhang. An Overview of Massive MIMO: Benefits and Challenges. IEEE journal of selected topics in signal processing, vol.8, no.5, 742-758.

상세보기
Rusek, F., Persson, D., Buon Kiong Lau, Larsson, E. G., Marzetta, T. L., Tufvesson, F.. Scaling Up MIMO: Opportunities and Challenges with Very Large Arrays. IEEE signal processing magazine, vol.30, no.1, 40-60.

상세보기
Sohrabi, Foad, Yu, Wei. Hybrid Digital and Analog Beamforming Design for Large-Scale Antenna Arrays. IEEE journal of selected topics in signal processing, vol.10, no.3, 501-513.

상세보기
El Ayach, Omar, Rajagopal, Sridhar, Abu-Surra, Shadi, Zhouyue Pi, Heath, Robert W.. Spatially Sparse Precoding in Millimeter Wave MIMO Systems. IEEE transactions on wireless communications, vol.13, no.3, 1499-1513.

상세보기
Sung, Lakju, Park, Daehee, Cho, Dong-Ho. Limited Feedback Hybrid Beamforming Based on Dual Polarized Array Antenna. IEEE communications letters : a publication of the IEEE Communications Society, vol.22, no.7, 1486-1489.

상세보기
Alkhateeb, Ahmed, Leus, Geert, Heath, Robert W.. Limited Feedback Hybrid Precoding for Multi-User Millimeter Wave Systems. IEEE transactions on wireless communications, vol.14, no.11, 6481-6494.

상세보기
Han, Shuangfeng, I, Chih-lin, Xu, Zhikun, Rowell, Corbett. Large-scale antenna systems with hybrid analog and digital beamforming for millimeter wave 5G. IEEE communications magazine, vol.53, no.1, 186-194.

상세보기
Proc NIPS ImageNet classification with deep convolutional neural networks krizhevsky 2012 1097
Rappaport, Theodore S., Sun, Shu, Mayzus, Rimma, Zhao, Hang, Azar, Yaniv, Wang, Kevin, Wong, George N., Schulz, Jocelyn K., Samimi, Mathew, Gutierrez, Felix. Millimeter Wave Mobile Communications for 5G Cellular: It Will Work!. IEEE access : practical research, open solutions, vol.1, 335-349.

상세보기
Proc ICML Rectified linear units improve restricted Boltzmann machines nair 2010 1
Oh, Taeseok, Song, Changick, Jung, Jaehoon, Ahn, Minki, Kim, Joonsuk, Lee, Inkyu. A New RF Beam Training Method and Asymptotic Performance Analysis for Multi-User Millimeter Wave Systems. IEEE access : practical research, open solutions, vol.6, 48125-48135.

상세보기
arXiv 1412 6980 Adam: A method for stochastic optimization kingma 2014
Ren, Yuwei, Wang, Yingmin, Qi, Can, Liu, Yinjun. Multiple-Beam Selection With Limited Feedback for Hybrid Beamforming in Massive MIMO Systems. IEEE access : practical research, open solutions, vol.5, 13327-13335.

상세보기
2012
Spencer, Q.H., Swindlehurst, A.L., Haardt, M.. Zero-forcing methods for downlink spatial multiplexing in multiuser MIMO channels. IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society, vol.52, no.2, 461-471.

상세보기

LOADING...

활용도 분석정보

상세보기

다운로드

내보내기

활용도 Top5 논문

해당 논문의 주제분야에서 활용도가 높은 상위 5개 콘텐츠를 보여줍니다.
더보기 버튼을 클릭하시면 더 많은 관련자료를 살펴볼 수 있습니다.

원문 보기

AccessON 원문보기

원문 URL 링크

DOI : 10.1109/ACCESS.2020.2990317 [무료]
IEEE : 저널 > 논문 [무료]
AccessON : 저널

*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.

오픈액세스(OA) 유형

GOLD

오픈액세스 학술지에 출판된 논문

저작권 관리 안내

내보내기 메뉴

내보내기 구분

파일저장
인쇄
메일전송

구성항목

기본정보
상세정보

관리번호, 논문명, 저널/프로시딩명, 저자 , 발행년, 권, 호, 시작페이지, 끝페이지, 발행기관

저장형식

Text(ASCII format)
Excel format
RefWorks Direct Export
RIS format (for Reference Manager, ProCite, EndNote), Scholar's Aids, Mendeley

메일정보

받는사람 (필수): @
보내는사람 (선택): @
제목
내용: KISTI 검색결과 이메일 서비스

안내

총 건의 자료가 검색되었습니다.

다운받으실 자료의 인덱스를 입력하세요. (1-10,000)

검색결과의 순서대로 최대 10,000건 까지 다운로드가 가능합니다.

데이타가 많을 경우 속도가 느려질 수 있습니다.(최대 2~3분 소요)

다운로드 파일은 UTF-8 형태로 저장됩니다.
파일의 내용이 제대로 보이지 않을실 때는 웹브라우저 상단의 보기 -> 인코딩 -> 자동선택 여부를 확인하십시오.

Text(ASCII format)
Excel format

표제어: PCR

동의어: Packet Collision Rate

용어 설명 출처 목록 (6)

용어 설명: PCR은 세균 특이성이 있는 primer를 이용하여 적은 수의 세균이 있을지라도 쉽게 검출할 수 있는 유용한 방법이며, 이를 이용하여 구강 내 치면세균막이나 타액에서 직접 세균을 검출할 수 있게 되었다[8].

AI-Helper ※ AI-Helper는 을 사용합니다.

AI-Helper

안녕하세요, AI-Helper입니다. 좌측 "선택된 텍스트"에서 텍스트를 선택하여 요약, 번역, 용어설명을 실행하세요.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.

연합인증