Demo Data from VLDB 2026 / reviewers in the wild / expert
Zheren Long
dblp:164/8968
· DBLP profile ↗
2ranked-venue papers
2as first author
0since 2021 · last 2018
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer networks
1 paper |
Physical-layer communications · 77% Vehicular, aerial and satellite networks · 23% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Physical-layer communications
channel coding and estimation |
0.3 | 1 | 2018 | Turbo equalization based on joint Gaussian, SIC-MMSE and LMMSE for nonlinear satellite channels · Sci. China Inf. Sci. 2018 |
Physical-layer communications › equalization
turbo equalization |
0.3 | 1 | 2018 | Turbo equalization based on joint Gaussian, SIC-MMSE and LMMSE for nonlinear satellite channels · Sci. China Inf. Sci. 2018 |
Vehicular, aerial and satellite networks › satellite communication
nonlinear channel |
0.1 | 1 | 2018 | Turbo equalization based on joint Gaussian, SIC-MMSE and LMMSE for nonlinear satellite channels · Sci. China Inf. Sci. 2018 |
Vehicular, aerial and satellite networks
satellite communication |
0.1 | 1 | 2018 | Turbo equalization based on joint Gaussian, SIC-MMSE and LMMSE for nonlinear satellite channels · Sci. China Inf. Sci. 2018 |
Methods — techniques the papers use, named apart from their topics
joint gaussian · 0.3SIC-MMSE · 0.3LMMSE · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2018 | Turbo equalization based on joint Gaussian, SIC-MMSE and LMMSE for nonlinear satellite channels
Zheren Long, Hua Wang 0001, Nan Wu 0002, Jingming Kuang 0001 |
Sci. China Inf. Sci. | 1 |
| 2015 | Indirect Learning Hybrid Memory Predistorter Based on Polynomial and Look-Up-TableabstractBaseband predistortion is a popular and efficient method to linearize high power amplifier (HPA) in wireless communication systems. Polynomial (POLY) and look-up-table (LUT) are two methods to design baseband predistorter (PD). However, on the one hand, POLY-based method is complex to implement. On the other hand, LUT-based predistorter suffers convergence time and quantization error problem. In this paper, we propose a hybrid POLY and LUT predistorter for memory nonlinear system in wideband scenarios, it is also suitable for memoryless channel. Simulations show that the proposed hybrid structure outperforms the traditional one with lower complexity. Zheren Long, Hua Wang 0001, Ning Guan, Nan Wu 0002, Dongxuan He |
VTC Spring | 1 |