research introduction-tsinghua-linglong...

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/


Outline

Lab introduction1

Research works2

Results and awards3

Future works4


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


Key technology for 5G wireless communications– Massive MIMO– Millimeter-wave communications– Non-orthogonal multiple access (NOMA)

Compressive sensing and its application in wireless communications

Advanced prototype and demo platform Wireless communication theories and technologies

Research directions


Professor/Associated professor, Ph.D. supervisor

Post-doctoral/Ph. D./Master students

Research group

Linglong Dai


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


No. Country Organization Collaborator Achievements

1 UK University of Southampton

Prof. Lajos HanzoFellow of the Royal

Academy of EngineeringIEEE Fellow

3 joint papers1 joint project (ongoing)

2 USA The University ofTexas at Austin

Prof. Robert HeathIEEE Fellow

2 joint papersCo-supervisor of a Ph.D. student

3 Belgium KU LeuvenProf. Marc Moonen

IEEE Fellow 3 joint papers1 joint project (finished)

4 USA Columbia University

Prof. Xiaodong WangIEEE Fellow 3 joint papers

5 Japan Tohoku University Prof. Fumiyuki Adachi

IEEE FellowOne creator of WCDMA

3 joint papers

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


Research overview


Research on massive MIMO


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


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


PN sequence


2014 IEEE ICC Best Paper Award

2015 IEEE Trans. Broadcast. Best Paper Award



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


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)



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”



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


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



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)



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

}


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)


1p 2psNp

1Ts 1N −T0 K=T I


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.



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


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



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)



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


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


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)


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)


Publish 1 book and translate 1 book

Books


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


Top 10 out of 77 journals in the area of wireless communications

Rank Journal IF 2014

Ourresults

1 IEEE Wireless Communications 6.524 1 paper2 IEEE Communications Surveys and Tutorials 6.490 /3 IEEE Communications Magazine 4.460 2 papers

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


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)


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]


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

3 上行多用户时域同步频分多址接入方法 ZL201010129747.9 2013-01-30 Jian Song, Linglong Dai, Jian Fu, Zhixing Yang

4 基于广播电视网的物联网组网方法及其路由方法

ZL201010218532.4 2013-06-05 Zhaocheng Wang, Depeng Jin, Yong Li, Linglong Dai, Changyong Pan

5 基于时频二维训练的 OFDM 块传输方法 ZL 201110124559.1 2013-08-07 Zhaocheng Wang, Linglong Dai, Zhixing Yang

6 基于物理层管道技术的定位方法 ZL201110188004.3 2014-01-29 Zhaocheng Wang, Ruifeng Ma, Linglong Dai, Zhixing Yang

7 基于压缩感知理论的TDS-OFDM传输方法 ZL201210054244.9 2014-07-02 Zhixing Yang, Linglong Dai, Zhaocheng Wang, Changyong Pan

8 一种球解码方法及球解码器 ZL201310174342.0 2016-06-22 Chen Qian, Linglong Dai, ZhaochengWang

9 基于信道空时相关性的稀疏MIMO-OFDM信道估计方法

ZL201410025293.9 2017-01-11 Linglong Dai, Zhen Gao, Chao Zhang, Zhaocheng Wang

10 信道时域相关性低复杂度压缩感知的信道估计方法及装置

ZL201310745257.5 2017-02-15 Linglong Dai, Zhen Gao, Chao Zhang, Zhaocheng Wang

Patents


10 issued patentsNo. Title Number date Authors

1 一种多载波Large-Scale MIMO系统的发射信号配置及信道估计的方法和设备

201410099131.X 2014-03-17 Linglong Dai, Zhen Gao, ZhaochengWang

2 一种多载波Large-Scale MIMO系统的重叠导频方法

201410140828.7 2014-04-09 Linglong Dai, Zhen Gao, ZhaochengWang

3 低复杂度的毫米波MIMO模拟波束赋形方法 201510050035.0 2015-01-30 Xinyu Gao, Linglong Dai, Zhaocheng Wang, Jinhui Chen

4Interference Coordination of Small Cell

Cluster in Ultra Dense Networks (UDN) with Base Station (BS) Assisting Information

FAI16GB3312X 2016-04-30Yang Yang, Linglong Dai, Yuan

Zhang, Richard MacKenzie, and Mo Hao

5 一种低复杂度的毫米波波束空间收发设计方案 201610839401.5 2016-06-24 Xinyu Gao, Linglong Dai, Sen Wang, Shuangfeng Han, Chih-Lin I

6 一种基于子空间的码本设计方法 201610056982.5 2016-07-24 Wenqian Shen, Linglong Dai, Yi Shi, Leiming Zhang

7 基于角度的重叠导频设计方法 201610074557.7 2016-09-25 Linglong Dai, Chen Hu, Jianjun Li, Yi Shi, Leiming Zhang

8 基于多分辨率波束训练的模拟预编码方案 201610030768.4 2016-11-05 Wenqian Shen, Linglong Dai, Yi Shi, Leiming Zhang

9 低成本低能耗的模数混合波束赋型技术 201710034980.4 2017-01-18 Xinyu Gao, Linglong Dai, Yi Shi, Leiming Zhang

10 一种FD-MIMO系统中的参考信号设计方法 201710030892.3 2017-02-22 Jianjun Li, Linglong Dai, Sen Wang, Shuangfeng Han, Chih-Lin

Patents


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

37/46Linglong Dai: Introduction of Research on Broadband Wireless Communications Theories and Technologies 37

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

38/46Linglong Dai: Introduction of Research on Broadband Wireless Communications Theories and Technologies 38

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


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


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


Guest Editor, IEEE Journal on Selected Areas in Communications (topic: mmWave communications) (IF: 3.672)

Leading Guest Editor, IEEE Wireless Communications (topic: non-orthogonal multiple access) (IF: 5.125)

Academic services


Millimeter-wave massive MIMO technology– Channel estimation, channel feedback (codebook design), channel tracking– Hybrid analog/digital precoding

Research plan


Millimeter-wave massive MIMO with lens antenna array– Channel estimation, channel feedback (codebook design), channel tracking– Beam selection, hybrid analog/digital precoding

Research plan


Millimeter-wave massive MIMO technology– System-level software simulation platform– Hardware evaluation platform

Research plan


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


http://oa.ee.tsinghua.edu.cn/dailinglong/

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