1/46 Linglong Dai: Introduction of Research on Broadband Wireless Communications Theories and Technologies Introduction of Research on Broadband Wireless Communications Theories and Technologies Linglong Dai Tsinghua National Laboratory for Information Science and Technology, Department of Electronic Engineering, Tsinghua University March 2017 Tsinghua University http://oa.ee.tsinghua.edu.cn/dailinglong/
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1/46Linglong Dai: Introduction of Research on Broadband Wireless Communications Theories and Technologies
Introduction of Research on Broadband Wireless Communications
Theories and TechnologiesLinglong Dai
Tsinghua National Laboratory for Information Science and Technology,Department of Electronic Engineering,
Tsinghua University
March 2017
Tsinghua University
http://oa.ee.tsinghua.edu.cn/dailinglong/
2/46Linglong Dai: Introduction of Research on Broadband Wireless Communications Theories and Technologies
Outline
Lab introduction1
Research works2
Results and awards3
Future works4
3/46Linglong Dai: Introduction of Research on Broadband Wireless Communications Theories and Technologies
Department of Electronic Engineering, Tsinghua University– University ranking: No. 25 in the world, QS 2016– EE ranking: No. 15 in the world, QS 2016
Tsinghua National Laboratory for Information Science and Technology (TNList)– The previous State Key laboratory of Microwave and Digital Communications
Affiliation
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Compressive sensing and its application in wireless communications
Advanced prototype and demo platform Wireless communication theories and technologies
Research directions
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Professor/Associated professor, Ph.D. supervisor
Post-doctoral/Ph. D./Master students
Research group
Linglong Dai
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Consider both the basic research as well as the major industrial applications– Basic research on the fundamental theory with international academic
impact, as well as the potential application in strategical industry of China– Supported by governments (e.g., 973 plan, 863 program, NSFC, etc.) as
well as industry (e.g., China Mobile, Huawei, etc.)
Major projects– The first group of 863 projects on 5G (2014-2016, 28 million RMB)– The first 973 project on VLC (2013-2017, 33 million RMB)– Principle investigator (PI) of many national projects from NSFC, MOST
Research feature
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No. Country Organization Collaborator Achievements
6 Singapore SUTD Asst. Prof. Chau Yuen Expert in MIMO
4 joint papers1 joint project (ongoing)
7 UK University of Southampton
Prof. Sheng ChenFellow of the Royal
Academy of EngineeringIEEE Fellow
4 joint papers
International cooperation
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Research overview
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Research on massive MIMO
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Challenge– TDS-OFDM is the key technology of DTMB systems, which can only support 64
QAM and lower order modulation– In long-latency channel, existing algorithms cannot cancel the interference
between training symbols and data symbols– DTMB cannot support 256 QAM and higher order modulation
Work 1: OFDM based on compressive sensing (CS)
训练序列 OFDM数据块
相互干扰
Training symbols Data symbols
Interference
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Our solution– We do not aim to cancel interference as much as possible – Utilize a small number of inference-free symbols to estimate the high-
dimension channel based on the channel sparsity– Utilize the temporal correlation of channel and distributed CS algorithm
to improve the channel estimation accuracy
Results– Support 256 QAM and improve spectral efficiency by 30%– Publish 1 IEEE JSAC (IF: 4.138) and 1 IEEE TSP (IF: 3.198)
PN sequence OFDM data block
Channel sparsity, temporal correlation
Estimate channel based on distributed CS algorithm
a small number inference-free symbols
iy 1i R+ −yjy
= +Y ΦH N
OFDM data block
Work 1: OFDM based on compressive sensing (CS)
PN sequence
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2014 IEEE ICC Best Paper Award
2015 IEEE Trans. Broadcast. Best Paper Award
Work 1: OFDM based on compressive sensing (CS)
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Challenge– Conventional MIMO: the pilot overhead of orthogonal pilot is proportional
to the number of antennas: 8 antennas in LTE-A 25%– Massive MIMO: unaffordable pilot overhead: 32 antennas 100%
Work 2: Overlapped pilot based on structural CS
100% pilot !
Large antenna array
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Our solution– Exploit the structural sparsity of massive MIMO channel in the angle domain– Transmitter: employ overlapped pilot instead of orthogonal pilot – Receiver: Distributed CS based channel estimation with low pilot overhead
Results– Pilot overhead is proportional to the small channel angle spread, reduced by 75%– Publish 1 IEEE JSAC (IF: 4.138), 1 TSP (IF: 3.198), and 1 TCOM (IF: 1.992)
Work 2: Overlapped pilot based on structural CS
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2014 USRI GASS Young Scientist Award
Fellow of the Canadian Academy of Engineering, Editor of IEEE TCOM, IEEE Fellow, Professor of University of British Columbia, Robert Schober comments our work as
“an elegant solution to the challengingproblem of channel estimation for massiveMIMO”
Work 2: Overlapped pilot based on structural CS
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Challenge– User estimates its channel independently, and feds it back to the base
station feedback overhead is proportional to the antenna number– Massive MIMO with large antenna number unaffordable overhead
User first estimates itschannel and feds it back
Work 3: Multi-user channel feedback based on lowrank matrix reconstruction
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Our solution– User directly feds the received pilots back to the base station– Base station exploits the low-rank property of multi-user channel, and
reconstructs the channel with low pilot overhead
Results– Reduce the feedback overhead by 30% and transform the complicated
calculation from the users side to the base station side– Publish 1 IEEE TSP (IF: 3.198, ESI top 1% highly cited paper)
k k k= +y h Φ n
ky
1 2[ , , , ]T T T TK=H h h h
k
K
Work 3: Multi-user channel feedback based on lowrank matrix reconstruction
18/46Linglong Dai: Introduction of Research on Broadband Wireless Communications Theories and Technologies
IEEE Fellow, Chief of IEEE Communication Society TechnicalCommittee, the winner of 2012 IEEE Signal Processing MagazineBest Paper Award, Professor of University of Texas at Austin, RobertW. Heath, Jr. comments our work as
[16] Zhen Gao, Linglong Dai, Zhaocheng Wang, and Sheng Chen, “Spatially common sparsity based adaptive channel estimation and feedback for FDD massive MIMO,” IEEE Trans. Signal Process., vol. 63, no. 23, pp. 6169–6183, Dec. 2015.
This work is considered as one of thetwo representative works in this area.The other one is a TCOM paperpublished by Prof. David J. Love fromPurdue University (IEEE Fellow)
Work 3: Multi-user channel feedback based on lowrank matrix reconstruction
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Challenge– Conventional MIMO: each antenna requires one dedicated RF chain– Hybrid precoding can significantly reduce the number of RF chains– Optimal hybrid precoding is difficult to obtain due to the non-convex
constraints in sub array architecture
Work 4: Hybrid precoding based on successiveinterference cancelation
}
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Our solution– Decompose the non-convex total optimization problem into a series of
convex sub-problems, each of which only considers one sub-array– Optimize each sub-problem one-by-one based on the idea of SIC– Reduce the complexity based on the asymptotic orthogonality of channel
Results– Achieve near-optimal sum-rate with only 10% computational complexity– Publish 1 IEEE JSAC (IF: 4.138, ESI top 1% highly cited paper)
Work 4: Hybrid precoding based on successiveinterference cancelation
1p 2psNp
1Ts 1N −T0 K=T I
21/46Linglong Dai: Introduction of Research on Broadband Wireless Communications Theories and Technologies
2015 IEEE RADIO Young Scientist Award
Editor of IEEE TWC, IEEE Fellow, Dean of Engineering inHKUST Prof. Khaled B. Letaief comments our work as
[31] Xinyu Gao, Linglong Dai, Shuangfeng Han, and Chih-Lin I, and R. W. Heath, “Energy-Efficient Hybrid Analog and Digital Precoding for mmWave MIMO Systems with Large Antenna Arrays,” IEEE J. Sel. Areas Commun., vol. 34, no. 4, pp. 998-1009, Apr. 2016.
For comparison, the SIC-basedmethod proposed in [31] is adoptedas a benchmark for the partiallyconnected structurer.
Work 4: Hybrid precoding based on successiveinterference cancelation
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Challenge– Beamspace MIMO can significantly reduce the number of RF chains,
but requires accurate information of beamspace channel– Large-size (256×16) beamspace channel is estimated with limited
number of RF chains (16) → Unaffordable pilot overhead– Beamspace MIMO and hybrid precoding have different architectures Channel estimation schemes for hybrid precoding cannot work
Work 5: Sparse beamspace channel estimation in lens-based MIMO system
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Our solution– Decompose the channel estimation problem into a series of sub-problems Each sub-problem considers only one sparse channel component
– Utilize the structural sparsity of beamspace channel Support of each channel component can be estimated with high accuracy Nonzero elements of each channel component can be estimated with low overhead
Pr
obab
ility
( ), 1kh ( ), 2kh ( )( )T, Cardkh
Work 5: Sparse beamspace channel estimation in lens-based MIMO system
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Results– Reduce pilot overhead by 50% and enjoy satisfying accuracy even in the low
SNR region– With the estimated channel, beamspace MIMO can achieve the performance
quite close to the fully digital MIMO– Publish 1 IEEE TWC paper (IF: 2.925 )– 2016 IEEE WCSP Best Paper Award (10/331)
Work 5: Sparse beamspace channel estimation in lens-based MIMO system
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Challenge– Conventional scheduling has high latency and signaling overhead – Uplink NOMA is expected to be scheduling-free– Base station needs to detect active users
Our solution– Experimental results show the natural sparsity among active users– Transform the active user detection into sparse signal recovery problem– Frame structure: structural sparsity, simultaneously detect multiple instants– Burst transmission: correlation between adjacent instant, dynamic detection
Works 6: Low latency scheduling-free NOMA based on CS
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Results– Linglong Dai, Bichai Wang, Yifei Yuan, Shuangfeng Han, Chih-Lin I, and
Zhaocheng Wang, “Non-orthogonal multiple access for 5G: solutions, challenges, opportunities, and future research trends,” IEEE Communications Magazine, vol. 53, no. 9, pp. 74-81, Sep. 2015. (IF:5.125)
– Bichai Wang, Linglong Dai, Talha Mir, and Zhaocheng Wang, “Joint user activity and data detection based on structured compressive sensing for NOMA,” IEEE Communications Letters, vol. 20, no. 7, pp. 1473–1476, Jul. 2016.
– Bichai Wang, Linglong Dai, Yuan Zhan, Talha Mir, and Jianjun Li, “Dynamic compressive sensing based multi-user detection for uplink grant-free NOMA,” IEEE Communications Letters, vol.20, no. 11, pp. 2320-2323, Nov. 2016.
121 times of Google Scholar citation,ESI top 1% highly cited paper
Works 6: Low latency scheduling-free NOMA based on CS
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New generation wireless broadcasting standard DTMB-A has ben submitted to ITU in Dec. 2013
DTMB-A has been adopted by ITU in Jul. 2015 Our works on OFDM are two key technologies in DTMB-A
Important application (1)
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Our works on pilot design, channel feedback, and precoding have been realized on the prototype built by China Mobile
Our work has won the 2016 Second Prize of Science and Technology Award (Natural Science) of China Institute of Communications (the first author)
Important application (2)
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Publish 1 book and translate 1 book
Books
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Publish 120 papers (81 journal papers and 39 conference papers) 66 IEEE journal papers 10 IEEE Journal on Selected Areas in Communications papers
Paper publication
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Top 10 out of 77 journals in the area of wireless communications
4 IEEE Journal on Selected Areas in Communications 4.138 10 papers
5 IEEE Network 3.720 /6 IEEE Transactions on Mobile Computing 2.912 /7 IEEE/OSA Journal of Lightwave Technology 2.862 3 papers8 IEEE Transactions on Wireless Communications 2.762 2 papers9 IEEE Transactions on Broadcasting 2.652 6 papers10 IEEE Transactions on Vehicular Technology 2.642 13 papers
Note:The impact factor and journal rank can be found on the official website of Journal Citation Reports(JCR)
Paper publication
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Representative papers
1. Jiayi Zhang, Linglong Dai, Xinlin Zhang, Emil Bjornson, and Zhaocheng Wang, “Achievable Rate of Rician Large-Scale MIMOChannels with Transceiver Hardware Impairments,” IEEE Transactions on Vehicular Technology, vol. 65, no. 10, pp. 8800-8806, Oct. 2016. (IF: 2.642)
2. Wenqian Shen, Linglong Dai, Yi Shi, Byonghyo Shim, and Zhaocheng Wang, “Joint Channel Training and Feedback for FDDMassive MIMO Systems,” IEEE Transactions on Vehicular Technology, vol. 65, no. 10, pp. 8762-8767, Oct. 2016. (IF: 2.642)
3. Zhen Gao, Linglong Dai, Zhaocheng Wang, Sheng Chen, and Lajos Hanzo, “Compressive Sensing Based Multi-User Detectorfor Large-Scale SM-MIMO Uplink,” IEEE Transactions on Vehicular Technology, vol. 65, no. 10, pp. 8725-8730, Oct. 2016.(IF: 2.642)
4. Xinyu Gao, Linglong Dai, Chau Yuen, and Zhaocheng Wang, “Turbo-Like Beamforming Based on Tabu Search Algorithm forMillimeter-Wave Massive MIMO Systems,” IEEE Transactions on Vehicular Technology, vol. 65, no. 7, pp. 5731-5737, Jul.2016. (IF: 2.642)
5. Xinyu Gao, Linglong Dai, Shuangfeng Han, Chih-Lin I, and Robert W. Heath, “Energy-Efficient Hybrid Analog and DigitalPrecoding for mmWave MIMO Systems with Large Antenna Arrays,” IEEE Journal on Selected Areas in Communications,vol. 34, no. 4, pp. 998-1009, Apr. 2016. (IF: 4.138) [ESI top 1% highly cited paper]
6. Zhen Gao, Linglong Dai, Wei Dai, Byonghyo Shim, and Zhaocheng Wang, “Structured Compressive Sensing Based Spatio-Temporal Joint Channel Estimation for FDD Massive MIMO,” IEEE Transactions on Communications, vol. 64, no. 2, pp. 601-617, Feb. 2016. (IF: 1.992) [ESI top 1% highly cited paper]
7. Zhen Gao, Linglong Dai, Zhaocheng Wang, and Sheng Chen, “Spatially Common Sparsity Based Adaptive Channel Estimation and Feedback for FDD Massive MIMO”, IEEE Transactions on Signal Processing, vol. 63, no. 23, pp. 6169-6183, Dec. 2015. (IF: 3.198)
8. Zhen Gao, Linglong Dai, De Mi, Zhaocheng Wang, Muhammad Ali Imran, and Muhammad Zeeshan Shakir, “MmWave Massive MIMO Based Wireless Backhaul for 5G Ultra-Dense Network,” IEEE Wireless Communications, vol. 22, no. 5, pp. 13-21, Oct. 2015. (IF: 6.524)
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Representative papers
9. Linglong Dai, Xinyu Gao, Xin Su, Shuangfeng Han, Chih-Lin I, and Zhaocheng Wang, “Low-Complexity Soft-Output Signal Detection Based on Gauss-Seidel Method for Uplink Multi-User Large-Scale MIMO Systems,” IEEE Transactions on Vehicular Technology, vol. 64, no. 10, pp. 4839-4845, Oct. 2015. (IF: 2.642)
10.Xinyu Gao, Linglong Dai, Yuting Hu, Yu Zhang, and Zhaocheng Wang, “Low-Complexity Signal Detection for Large-Scale MIMO in Optical Wireless Communications,” IEEE Journal on Selected Areas in Communications, vol. 33, no. 9, pp. 1903-1912, Sep. 2015. (IF: 4.138)
11.Jiayi Zhang, Linglong Dai, Yanjun Han, Yu Zhang, and Zhaocheng Wang, “On the Ergodic Capacity of MIMO Free-Space Optical Systems over Turbulence Channels,” IEEE Journal on Selected Areas in Communications, vol. 33, no. 9, pp. 1925-1934, Sep. 2015. (IF: 4.138)
12.Linglong Dai, Bichai Wang, Yifei Yuan, Shuangfeng Han, Chih-Lin I, and Zhaocheng Wang, “Non-Orthogonal Multiple Access for 5G: Solutions, Challenges, Opportunities, and Future Research Trends,” IEEE Communications Magazine, vol. 53, no. 9, pp. 74-81, Sep. 2015. . (IF: 4.460) [ESI top 1% highly cited paper]
13.Jiayi Zhang, Linglong Dai, Yu Zhang, and Zhaocheng Wang, “Unified Performance Analysis of Mixed Radio Frequency/Free-Space Optical Dual-Hop Transmission Systems,” IEEE/OSA Journal of Lightwave Technology, vol. 33, no. 11, pp. 2286-2293, June 2015. (IF: 2.862)
14.Linglong Dai, Jintao Wang, Zhaocheng Wang, P. Tsiaflakis, and M. Moonen, “Spectrum- and Energy-Efficient OFDM Based on Simultaneous Multi-Channel Reconstruction,” IEEE Transactions on Signal Processing, vol. 61, no. 23, pp. 6047-6059, Dec. 2013. (IF: 3.198)
15.Linglong Dai, Zhaocheng Wang, and Zhixing Yang, “Compressive Sensing Based Time Domain Synchronous OFDM Transmission for Vehicular Communications,” IEEE Journal on Selected Areas in Communications, vol. 31, no. 9, pp. no. 460-469, Sep. 2013. (IF: 4.138)
16.Linglong Dai, Zhaocheng Wang, and Zhixing Yang, “Spectrally Efficient Time-Frequency Training OFDM for Mobile Large-Scale MIMO Systems,” IEEE Journal on Selected Areas in Communications, vol. 31, no. 2, pp. 251-263, Feb. 2013. (IF: 4.138) [ESI top 1% highly cited paper]
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10 granted patentsNo. Title Number date Authors
1 时域同步正交频分复用系统中的 CP-OFDM 信号重构方法及装置
ZL200810118118.9 2011-12-28 Jian Fu, Linglong Dai, Jian Song, Jun Wang, Jintao Wang, Zhixing Wang
2 TOA 定位的估计方法及基于该估计方法的精确定位方法
ZL200910237602.8 2012-02-08 Zhaocheng Wang, Linglong Dai, Jun Wang, Zhixing Yang
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Google scholar citation: 1500+, H-index: 21 Web of Science citation: 800+ ESI top 1% Highly Cited Papers: 4 The citers include 8 academicians from U. S., Canada, U. K., 10
Editors of the top IEEE journals, and 40 IEEE Fellows
Paper citation
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2016 Second Prize of Science and Technology Award of China Institute of Communications (the first author)
2016 IEEE Transactions on Communications Outstanding reviewer 2016 IEEE Communications Letters Outstanding reviewer 2016 IEEE WCSP Best Paper Award (top 1.3%) 2016 URSI AP-RASC Young Scientist Award 2015 IEEE RADIO Young Scientist Award 2015 IEEE Trans. Broadcast. Best Paper Award 2014 URSI GASS Young Scientist Award 2014 IEEE ICC Best Paper Award (top 1.8%)
Awards
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2013 National Excellent Doctoral Dissertation Award (Nomination) 2013 IEEE ICC Best Paper Award (top 1.3%) 2013 Excellent postdoctoral (Top 0.8%) 2012 Beijing Excellent Doctoral Dissertation Award 2011 Excellent graduates of Tsinghua University 2011 Tsinghua Excellent Doctoral Dissertation Award 2011 Academic rookie (Top 0.05%) 2010 First price of GE Technology Innovation Award 2010 First class of comprehensive scholarship 2009 Outstanding student leaders
Awards
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No. Project name Project source Time Role
1 Key Signal Processing Technologies for 5G Millimeter-Wave Massive MIMO with Lens Antenna Array
Royal Academy of Engineering 2017/04-2019/03 PI
2 New Multiple Access Technique with Massive Connectivity and Low Latency for 5G
Korea National Research Foundation 2017/03/-2019/02 PI
3 Research on Non-Orthogonal Multiple Access (NOMA) for 5G
National Natural Science Foundation 2016/01-2019/12 PI
4 Basic Theory for Capacity-Approaching and Highly Reliable Broadband Spectrum Communication
National Key Basic Research Program 2015/01-2017/12 PI
5 5G System Design, Key Technology Research and Standardization China Mobile 2015/04-2016/04 PI
6 Green Heterogeneous Sensor Networks Based on Broadcasting for Smart City China MOST 2015/04-2017/03 PI
7 Spectrum-Efficient TFT-OFDM Based on Time-Frequency Training
National Natural Science Foundation 2013/01-2015/12 PI
8 Transmission Mechanism and Channel Capacity Analysis of Broadband Spectrum Communication
National Key Basic Research Program 2013/01-2014/12 PI
9 Key Person Supporting Program of Tsinghua University Tsinghua University 2013/07-2015/06 PI
10Compressive Sensing Based Time Domain
Synchronous OFDM for Next Generation Digital Television Standard
International Cooperation Foundation 2012/07-2014/06 PI
Projects
40/46Linglong Dai: Introduction of Research on Broadband Wireless Communications Theories and Technologies
Editor, IEEE Transactions on Communications (IF: 2.298)
Editor, IEEE Transactions on Vehicular Technology (IF: 2.243)
Editor, IEEE Communications Letters (IF: 1.291 )
Co-Chair, 5G Signal Processing of IEEE Communications Society
Academic services
41/46Linglong Dai: Introduction of Research on Broadband Wireless Communications Theories and Technologies
Guest Editor, IEEE Journal on Selected Areas in Communications (topic: mmWave communications) (IF: 3.672)
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Summary
Category Performance
Research platform Tsinghua National Laboratory for Information Science and Technology (TNList),Department of Electronic Engineering, Tsinghua University
Research direction Key technology for 5G wireless communications, e.g., Massive MIMO Millimeter-wave communications, Non-orthogonal multiple access (NOMA)
Research group 4 Professor/Associated Professors, 1 Post-doctoral fellow, 7 Ph.D students, 1 master student
Research feature Basic research and industrial applications
Publications 120 papers (81 journal papers, and 39 conference papers), 65 IEEE papers, 10 IEEE JSAC papers, 1 English book, 4 book chapters
Patens 10 granted patens, 10 issued patents
Citations Google scholar citation: 1500+, H-index: 21; Web of Science citation: 800+, ESI top 1% Highly Cited Papers: 4
Research Projects Principle Investigators (PIs) of over 10 projects funded by NSFC, MOST, etc.
Research Awards2016 Second Prize of Science and Technology Award of CIC; 2015 IEEE Transactions on Broadcasting Best Paper Award; IEEE ICC 2014 Best Paper Award; 2014 National Excellent Doctoral Dissertation Nomination Award; IEEE ICC 2013 Best Paper Award
Academic Services Editor of IEEE TCOM, TVT, CL; Guest Editor of IEEE JSAC (SI on mmWave), WCOM (SI on NOMA); Co-Chair of 5G Signal Processing of IEEE Communications Society
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