VLDB 2026 Research / reviewers in the wild / expert
Rina Su
dblp:63/4062
· DBLP profile ↗
11ranked-venue papers
8as first author
8since 2021 · last 2026
—ORCID · conflict
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 5 · 5 first-author · 3 since 2021Applied, interdisciplinary, general and emerging computing · 3 · 2 first-author · 3 since 2021Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Security and privacy · 1
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
4 papers |
Internet architecture and protocols · 64% Wireless networking · 18% Physical-layer communications · 16% |
Topics — the 7 heaviest of 8, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Internet architecture and protocols
network coding |
2.9 | 4 | 2026 | GRAND-Assisted Random Linear Network Coding in Wireless Broadcasts · IEEE Trans. Commun. 2026 Lightweight Instantly Decodable Network Coding: Performance Analysis and Algorithm Design · IEEE Trans. Commun. 2025 Completion Delay of Random Linear Network Coding in Full-Duplex Relay Networks · IEEE Trans. Commun. 2022 |
Internet architecture and protocols › network coding
random linear network coding |
2.0 | 3 | 2026 | GRAND-Assisted Random Linear Network Coding in Wireless Broadcasts · IEEE Trans. Commun. 2026 Completion Delay of Random Linear Network Coding in Full-Duplex Relay Networks · IEEE Trans. Commun. 2022 Delay-Complexity Trade-Off of Random Linear Network Coding in Wireless Broadcast · IEEE Trans. Commun. 2020 |
Wireless networking
broadcast |
1.6 | 3 | 2026 | Lightweight Instantly Decodable Network Coding: Performance Analysis and Algorithm Design · IEEE Trans. Commun. 2025 Delay-Complexity Trade-Off of Random Linear Network Coding in Wireless Broadcast · IEEE Trans. Commun. 2020 GRAND-Assisted Random Linear Network Coding in Wireless Broadcasts · IEEE Trans. Commun. 2026 |
Physical-layer communications
channel coding and estimation |
1.0 | 1 | 2026 | GRAND-Assisted Random Linear Network Coding in Wireless Broadcasts · IEEE Trans. Commun. 2026 |
Internet architecture and protocols › network coding
instantly decodable network coding |
0.9 | 1 | 2025 | Lightweight Instantly Decodable Network Coding: Performance Analysis and Algorithm Design · IEEE Trans. Commun. 2025 |
Physical-layer communications
decoding delay |
0.3 | 1 | 2025 | Lightweight Instantly Decodable Network Coding: Performance Analysis and Algorithm Design · IEEE Trans. Commun. 2025 |
Physical-layer communications › channel coding › decoding algorithms
decoding complexity |
0.1 | 1 | 2020 | Delay-Complexity Trade-Off of Random Linear Network Coding in Wireless Broadcast · IEEE Trans. Commun. 2020 |
Methods — techniques the papers use, named apart from their topics
lower bound derivation · 1.9syndrome generation · 1.0algorithm design · 0.9numerical simulation · 0.6closed-form analysis · 0.6systematic coding · 0.4circular-shift operations · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Instantly Decodable Network Coding with Limited Feedback
Limin Wen, Rina Su, Qifu Tyler Sun, Shaoteng Liu |
ISIT | 2 |
| 2026 | GRAND-Assisted Random Linear Network Coding in Wireless BroadcastsabstractIn the study of packet-level random linear network coding (RLNC) in wireless broadcast, RLNC over GF(2L) is known to asymptotically achieve the optimal completion delay with increasingL. Effective utilization of guessing random additive noise decoding (GRAND) at the physical layer can help leverage RLNC packets to generate syndromes so as to reduce packet erasure probabilities and thus further improve the completion delay performance. Prior to this work, only a few studies investigated GRAND-assisted RLNC and they restricted to GF(2)-coding. In this paper, we first provide a general framework to formulate the decoding process of GRAND-assisted RLNC over GF(2L) forL≥ 1. Even for GRAND-assisted GF(2)-RLNC, the formulation is more complete than previous considerations in the sense that it takes the a priori information of which packets have errors into consideration. Moreover, we propose a novel GRAND-assisted GF(2L)-RLNC scheme whose computational overhead introduced by GRAND is negligible. In particular, a subset of GF(2L) is carefully designed for random coding coefficient selection. For the novel scheme, we theoretically derive lower bounds on the distribution as well as an upper bound on the expected value of the completion delay. Numerical results demonstrate not only a reduction in average completion delay for the novel scheme, but also the advantage of random coding coefficient selection from the specially designed subset for GRAND-assisted RLNC schemes. Rina Su, Qifu Tyler Sun, Mingshuo Deng, Jinhong Yuan |
IEEE Trans. Commun. | 1 |
| 2025 | Lightweight Instantly Decodable Network Coding: Performance Analysis and Algorithm DesignabstractWe consider broadcasting a block of data packets to multiple users via instantly decodable network coding (IDNC) under the semi-online feedback transmission mode. In this paper, we first introduce a new class of IDNC schemes called lightweight IDNC, tailored for wireless broadcast with stringent computational load at the receiver end. Unlike traditional IDNC that may encode a larger number of original packets together, lightweight IDNC limits each coded packet to a combination of at most two original packets. Explicit lower bounds of the total completion delay as well as the decoding delay are respectively obtained for arbitrary lightweight IDNC schemes. We further investigate the number of transmission rounds as another performance metric, and explicitly characterize its distribution and expectation. The characterizations apply to arbitrary partition-based IDNC schemes, including the lightweight IDNC schemes considered in this paper. A new efficient algorithm is also proposed to construct lightweight IDNC schemes which grants the original packets with lower coding opportunity a higher priority to be encoded. Numerical analyses demonstrate that the lightweight IDNC schemes constructed by the new algorithm not only achieve lower completion and decoding delays in comparison with the ones constructed by the existing algorithm but also adhere closely to theoretical lower bounds, demonstrating their efficiency and practical utility. Rina Su, Qifu Tyler Sun, Shaoteng Liu, Zhongshan Zhang, Linqi Song |
IEEE Trans. Commun. | 1 |
| 2024 | GRAND-Assisted Random Linear Network Coding in Wireless BroadcastsabstractIn the study of packet-level random linear network coding (RLNC) in wireless broadcast, RLNC over$\mathbf{GF}(2^{L})$is known to asymptotically achieve the optimal completion delay with increasing$L$. Utilization of guessing random additive noise decoding (GRAND) at physical layer can help leverage RLNC packets to generate syndromes so as to reduce packet erasure probabilities and thus further improve the completion delay performance. Prior to this work, only few studies investigated GRAND-assisted RLNC and they restricted to GF(2)-coding. In this paper, we first provide a general framework to formulate the decoding process of GRAND-assisted RLNC over$\mathbf{GF}(2^{L})$for$L\geq 1$. Even for GRAND-assisted GF(2)-RLNC, the formulation is more complete than previous considerations in the sense that it takes the a priori information of which packets have errors into consideration. In addition, we propose a novel GRAND-assisted$\mathbf{GF}(2^{L})$-RLNC scheme whose computational overhead introduced by GRAND is negligible. We theoretically derive lower bounds on the distribution as well as an upper bound on the expected value of the completion delay of the proposed scheme. Numerical results also demonstrate a reduction in average completion delay for the proposed new GF(28)-RLNC scheme, when compared to existing approaches. Rina Su, Qifu Tyler Sun, Mingshuo Deng, Zhongshan Zhang, Jinhong Yuan |
ISIT | 1 |
| 2024 | Lightweight Instantly Decodable Network Coding in Wireless BroadcastabstractWe consider broadcasting a block of data packets to multiple users via instantly decodable network coding (IDNC) under the semi-online feedback transmission mode. In this paper, we first introduce a new class of IDNC schemes called lightweight IDNC, tailored for wireless broadcast with stringent computational load at the receiver end. Unlike traditional IDNC that may encode a larger number of original packets together, lightweight IDNC limits each coded packet to a combination of at most two original packets. We obtain lower bounds on the total completion delay that apply to arbitrary lightweight IDNC schemes. We further investigate the number of transmission rounds as another performance metric, and explicitly characterize its distribution and expectation. The characterizations apply to arbitrary partition-based IDNC schemes, including the lightweight IDNC schemes considered in this paper. A new efficient algorithm is also proposed to construct lightweight IDNC schemes which grants the original packets with lower coding opportunity a higher priority to be encoded. Numerical analyses demonstrate that the lightweight IDNC schemes constructed by the new algorithm not only achieve lower completion and decoding delays in comparison with the ones constructed by the existing algorithm but also adhere closely to theoretical lower bounds, demonstrating their efficiency and practical utility. Rina Su, Qifu Tyler Sun, Shaoteng Liu, Zhongshan Zhang, Linqi Song |
VTC Spring | 2 |
| 2024 | Kernel correlation-dissimilarity for Multiple Kernel k-Means clustering
Rina Su, Yu Guo 0008, Caiying Wu, Qiyu Jin, Tieyong Zeng |
Pattern Recognit. | 1 |
| 2022 | Completion Delay of Random Linear Network Coding in Full-Duplex Relay NetworksabstractAs the next-generation wireless networks thrive, full-duplex and relay techniques are combined to improve the network performance. Random linear network coding (RLNC) is another popular technique to enhance the efficiency and reliability of wireless communications. In this paper, in order to explore the potential of RLNC in full-duplex relay networks, we investigate two fundamental perfect RLNC schemes and theoretically analyze their completion delay performance. The first scheme is a straightforward application of conventional perfect RLNC studied in wireless broadcast, so it involves no additional process at the relay. Its performance serves as an upper bound for all perfect RLNC schemes. The other scheme allows sufficiently large buffer and unconstrained linear coding at the relay. It attains the optimal performance and serves as a lower bound for all RLNC schemes. For both schemes, closed-form formulae to characterize the expected completion delay at a single receiver as well as for the whole system are derived. Numerical results are also demonstrated to validate the theoretical characterizations, and compare the two fundamental schemes with the existing one. Rina Su, Qifu Tyler Sun, Zhongshan Zhang, Zongpeng Li |
IEEE Trans. Commun. | 1 |
| 2021 | On the Delay of Random Linear Network Coding in Full-Duplex Relay NetworksabstractAs the next-generation wireless networks thrive, full-duplex and relaying techniques are combined to improve the network performance. Random linear network coding (RLNC) is another popular technique to enhance the efficiency and reliability in wireless communications. In this paper, in order to explore the potential of RLNC in full-duplex relay networks, we investigate two fundamental perfect RLNC schemes and theoretically analyze their completion delay performance. The first scheme is a straightforward application of conventional perfect RLNC studied in wireless broadcast, so it involves no additional process at the relay. Its performance serves as an upper bound among all perfect RLNC schemes. The other scheme allows sufficiently large buffer and unconstrained linear coding at the relay. It attains the optimal performance and serves as a lower bound among all perfect RLNC schemes. Closed-form formulae for the expected completion delay of both schemes are derived. Numerical results are also demonstrated to verify the theoretical characterization and compare the two new schemes with the existing one. Rina Su, Qifu Tyler Sun, Zhongshan Zhang |
ISIT | 1 |
| 2020 | Delay-Complexity Trade-off of Random Linear Network Coding in Wireless BroadcastabstractIn wireless broadcast, random linear network coding (RLNC) over GF(2L) is known to asymptotically achieve the optimal completion delay with increasing L. However, the high decoding complexity hinders the potential applicability of RLNC schemes over large GF(2L). In this paper, a comprehensive analysis of completion delay and decoding complexity is conducted for field-based systematic RLNC schemes in wireless broadcast. In particular, we prove that the RLNC scheme over GF(2) can also asymptotically approach the optimal completion delay per packet when the packet number goes to infinity. Moreover, we introduce a new method, based on circular-shift operations, to design RLNC schemes which avoid multiplications over large GF(2L). The new RLNC schemes turn out to have a much better trade-off between completion delay and decoding complexity. In particular, numerical results demonstrate that the proposed schemes can attain average completion delay just within 5% higher than the optimal one, while the decoding complexity is only about 3 times the one of the RLNC scheme over GF(2). Rina Su, Qifu Tyler Sun, Zhongshan Zhang |
ICC | 1 |
| 2020 | Delay-Complexity Trade-Off of Random Linear Network Coding in Wireless BroadcastabstractIn wireless broadcast, random linear network coding (RLNC) over GF(2L) is known to asymptotically achieve the optimal completion delay with increasing L. However, the high decoding complexity hinders the potential applicability of RLNC schemes over large GF(2L). In this paper, a comprehensive analysis of completion delay and decoding complexity is conducted for field-based systematic RLNC schemes in wireless broadcast. In particular, we prove that the RLNC scheme over GF(2) can also asymptotically approach the optimal completion delay per packet when the packet number goes to infinity. Moreover, we introduce a new method, based on circular-shift operations, to design RLNC schemes which avoid multiplications over large GF(2L). Based on both theoretical and numerical analyses, the new RLNC schemes turn out to have a much better trade-off between completion delay and decoding complexity. In particular, numerical results demonstrate that the proposed schemes can attain average completion delay just within 5% higher than the optimal one, while the decoding complexity is only about 3 times the one of the RLNC scheme over GF(2). Rina Su, Qifu Tyler Sun, Zhongshan Zhang |
IEEE Trans. Commun. | 1 |
| 2011 | Reform and Practice of Training Engineering Professionals in 2C+E Computer ScienceabstractIn order to meet the current demand for IT professionals, this paper presents the reform idea of 2C+E (Core professional knowledge, Core professional ability, Engineering environment) training system with the goal of training ideal professionals. The idea needs students to master core professional knowledge, to train core professional ability in the university, and to enhance their capability of engineering practice in the environment of university-enterprise cooperation. We have successfully applied reform measures of training engineering professionals in real situations and demonstrated a training scheme of university-enterprise cooperation. The result of the reform has played a significant role in training engineering professionals of computer major in our university. Jianbo Fan, Liangxu Liu, Rina Su |
TrustCom | 3 |