Fa-Long Luo

1.2k total citations · 1 hit paper
49 papers, 583 citations indexed

About

Fa-Long Luo is a scholar working on Electrical and Electronic Engineering, Artificial Intelligence and Signal Processing. According to data from OpenAlex, Fa-Long Luo has authored 49 papers receiving a total of 583 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Electrical and Electronic Engineering, 18 papers in Artificial Intelligence and 16 papers in Signal Processing. Recurrent topics in Fa-Long Luo's work include Neural Networks and Applications (17 papers), Blind Source Separation Techniques (12 papers) and PAPR reduction in OFDM (8 papers). Fa-Long Luo is often cited by papers focused on Neural Networks and Applications (17 papers), Blind Source Separation Techniques (12 papers) and PAPR reduction in OFDM (8 papers). Fa-Long Luo collaborates with scholars based in United States, China and Germany. Fa-Long Luo's co-authors include R. Unbehauen, Zheng Bao, Bernard Widrow, Andrzej Cichocki, Charlie Zhang, Chaslav V. Pavlovic, Jun Yang, Arye Nehorai, S. S. Melnyk and Marco Di Renzo and has published in prestigious journals such as IEEE Transactions on Signal Processing, IEEE Communications Surveys & Tutorials and IEEE Signal Processing Magazine.

In The Last Decade

Fa-Long Luo

47 papers receiving 550 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Terahertz Communications and Sensing for 6G and Beyond: A Comprehensive Review

2024 146 citations Fa-Long Luo et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Fa-Long Luo United States	11	253	174	148	96	89	49	583
K.M.M. Prabhu India	13	279 1.1×	290 1.7×	35 0.2×	146 1.5×	70 0.8×	83	730
Abhijit Chandra India	12	125 0.5×	198 1.1×	71 0.5×	119 1.2×	78 0.9×	71	505
J.G. McWhirter United Kingdom	12	180 0.7×	273 1.6×	51 0.3×	247 2.6×	64 0.7×	46	582
Stanley P. Lipshitz Canada	12	211 0.8×	229 1.3×	76 0.5×	120 1.3×	232 2.6×	53	690
W.H. Holmes Australia	13	124 0.5×	201 1.2×	77 0.5×	109 1.1×	88 1.0×	55	437
Philip Fong Canada	15	145 0.6×	56 0.3×	201 1.4×	81 0.8×	21 0.2×	39	628
Hussein Baher Ireland	9	209 0.8×	170 1.0×	27 0.2×	108 1.1×	119 1.3×	31	460
Nguyen T. Thao United States	11	135 0.5×	299 1.7×	68 0.5×	173 1.8×	166 1.9×	39	643
Jianhong Xiang China	14	118 0.5×	47 0.3×	73 0.5×	48 0.5×	26 0.3×	68	521
G. Mathew Singapore	15	234 0.9×	260 1.5×	167 1.1×	133 1.4×	38 0.4×	66	657

Countries citing papers authored by Fa-Long Luo

Since Specialization

Citations

This map shows the geographic impact of Fa-Long Luo's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Fa-Long Luo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fa-Long Luo more than expected).

Fields of papers citing papers by Fa-Long Luo

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Fa-Long Luo. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Fa-Long Luo. The network helps show where Fa-Long Luo may publish in the future.

Co-authorship network of co-authors of Fa-Long Luo

This figure shows the co-authorship network connecting the top 25 collaborators of Fa-Long Luo. A scholar is included among the top collaborators of Fa-Long Luo based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Fa-Long Luo. Fa-Long Luo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

1.

Luo, Fa-Long, et al.. (2019). The Industry Digital Signal Processing Technology Standing Committee [In the Spotlight]. IEEE Signal Processing Magazine. 36(6). 168–167. 1 indexed citations

2.

Luo, Fa-Long & Charlie Zhang. (2016). Energy-efficient Resource Allocation in 5G with Application to D2D. 616. 2 indexed citations

3.

Luo, Fa-Long & Charlie Zhang. (2016). Non-Orthogonal Multiple Access (NOMA): Concept and Design. 616. 12 indexed citations

4.

Luo, Fa-Long & Charlie Zhang. (2016). An Introduction to Modulations and Waveforms for 5G Networks. 616. 2 indexed citations

5.

Luo, Fa-Long & Charlie Zhang. (2016). Massive MIMO for 5G: Theory, Implementation and Prototyping. 616. 3 indexed citations

6.

Luo, Fa-Long & Charlie Zhang. (2016). Ultra Dense Networks: General Introduction and Design Overview. 616. 1 indexed citations

7.

Luo, Fa-Long. (2015). Signal Processing Techniques for 5G: An Overview. ZTE communications. 13(1). 20–27. 1 indexed citations

8.

Luo, Fa-Long, et al.. (2011). An Antenna Diversity Scheme for Digital Front-End with OFDM Technology. ZTE communications. 9(4). 32–34. 1 indexed citations

9.

Luo, Fa-Long, Hongxiang Li, Dimitra I. Kaklamani, et al.. (2011). Digital Front-End in Wireless Communications and Broadcasting. Cambridge University Press eBooks. 65 indexed citations

10.

Luo, Fa-Long & R. Unbehauen. (1999). Comments on: A unified algorithm for principal and minor components extraction. Neural Networks. 12(2). 393–393. 6 indexed citations

11.

Unbehauen, R., et al.. (1999). Electronically tunable generalized impedance converter structures. International Journal of Circuit Theory and Applications. 27(5). 517–522. 10 indexed citations

12.

Luo, Fa-Long, R. Unbehauen, & Yanda Li. (1997). Real-time computation of singular vectors. Applied Mathematics and Computation. 86(2-3). 197–214. 1 indexed citations

13.

Luo, Fa-Long, R. Unbehauen, & Andrzej Cichocki. (1997). A Minor Component Analysis Algorithm. Neural Networks. 10(2). 291–297. 59 indexed citations

14.

Luo, Fa-Long, Yanda Li, & Chunxiang He. (1996). Neural network approach to the TLS linear prediction frequency estimation problem. Neurocomputing. 11(1). 31–42. 5 indexed citations

15.

Luo, Fa-Long, et al.. (1995). Real-time neural computation of the eigenvector corresponding to the largest eigenvalue of positive matrix. Neurocomputing. 7(2). 145–157. 12 indexed citations

16.

Luo, Fa-Long, et al.. (1994). Brief communication A theorem concerning the energy function of Hopfield continuous-variable neural networks. International Journal of Electronics. 76(3). 443–446. 1 indexed citations

17.

Luo, Fa-Long, et al.. (1993). The ML bearing estimation by using neural networks. Journal of Electronics (China). 10(1). 1–8. 1 indexed citations

18.

Luo, Fa-Long & Zheng Bao. (1992). Real-time neural computation of the maximum likelihood criterion for bearing estimation problems. Neural Networks. 5(5). 765–769. 4 indexed citations

19.

Luo, Fa-Long & Zheng Bao. (1992). Neural network approach to computing matrix inversion. Applied Mathematics and Computation. 47(2-3). 109–120. 49 indexed citations

20.

Luo, Fa-Long, et al.. (1992). Real-time implementation of 'propagator' bearing estimation algorithm by use of neural network. IEEE Journal of Oceanic Engineering. 17(4). 320–325. 2 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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