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Runqian Wang

dblp:266/7977 · DBLP profile ↗
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2ranked-venue papers
2as first author
2since 2021 · last 2024
0000-0003-3856-6352ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Computer networks · 1 · 1 first-author · 1 since 2021

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
Cellular and mobile networks · 67% Wireless networking · 17% Network performance modeling · 17%
Artificial intelligence
1 paper
Efficient and distributed learning · 67% Transfer learning and domain adaptation · 33%

Topics — the 9 heaviest of 9, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Efficient and distributed learning › parameter-efficient fine-tuning
low-rank adaptation
0.812024
Trans-LoRA: towards data-free Transferable Parameter Efficient Finetuning · NeurIPS 2024
Machine learning › Transfer learning and domain adaptation › knowledge transfer
model transfer
0.812024
Trans-LoRA: towards data-free Transferable Parameter Efficient Finetuning · NeurIPS 2024
Machine learning › Efficient and distributed learning
parameter-efficient fine-tuning
0.812024
Trans-LoRA: towards data-free Transferable Parameter Efficient Finetuning · NeurIPS 2024
Cellular and mobile networks
device-to-device communication
0.512021
Towards a Deep Analysis of Millimeter Wave D2D Underlaid Cellular Networks · IEEE Trans. Commun. 2021
Network performance modeling › stochastic geometry modeling
meta distribution
0.512021
Towards a Deep Analysis of Millimeter Wave D2D Underlaid Cellular Networks · IEEE Trans. Commun. 2021
Cellular and mobile networks › millimeter-wave communication
millimeter wave cellular networks
0.512021
Towards a Deep Analysis of Millimeter Wave D2D Underlaid Cellular Networks · IEEE Trans. Commun. 2021
Cellular and mobile networks
millimeter-wave communication
0.512021
Towards a Deep Analysis of Millimeter Wave D2D Underlaid Cellular Networks · IEEE Trans. Commun. 2021
Wireless networking
stochastic geometry
0.512021
Towards a Deep Analysis of Millimeter Wave D2D Underlaid Cellular Networks · IEEE Trans. Commun. 2021
Cellular and mobile networks › device-to-device communication
underlaying cellular networks
0.512021
Towards a Deep Analysis of Millimeter Wave D2D Underlaid Cellular Networks · IEEE Trans. Commun. 2021

Methods — techniques the papers use, named apart from their topics

low-rank adaptation · 0.8nakagami fading · 0.5moment analysis · 0.5
YearPublicationVenuePosition
2024 Trans-LoRA: towards data-free Transferable Parameter Efficient Finetuning
Runqian Wang, Soumya Ghosh, David D. Cox, Diego Antognini, Aude Oliva, Rogério Feris, Leonid Karlinsky
NeurIPS1
2021 Towards a Deep Analysis of Millimeter Wave D2D Underlaid Cellular Networks
abstract
This paper provides a deep analysis for millimeter-wave device-to-device (mm-wave D2D) underlaid cellular networks to investigate how the performance of individual users or link percentiles under constraints are affected by the interaction between D2D and cellular communication modes integrated with the salient mm-wave features. Under a general Nakagami fading model, we derive the upper bounds for the$b$-moments of the conditional signal-to-interference-noise ratio (SINR) distributions for the two modes given the network realization, and further calculate the upper bounds of SINR meta distributions (MDs). Moreover, we extend the MD-based analysis to evaluate the fine-grained performance for the transmission effectiveness, through deriving the bounding results for the user-perceived rate MD and spatial rate capacity (SRC), the latter of which is defined as the effective density of users satisfying a given transmission rate requirement with a certain probability. Numerical results validate the tightness of the proposed bounds and reveal the different effects of the network configuration parameter associated with mm-wave on the link and network performances from the view of individual users.
Runqian Wang, Na Deng, Haichao Wei
IEEE Trans. Commun.1