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Yaqing Yan

dblp:363/8474 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · none

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

Computer networks · 1 · 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
Vehicular, aerial and satellite networks · 38% Network optimization and economics · 38% Wireless networking · 23%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Network optimization and economics
resource allocation
0.812024
Joint Optimization of Mobility and Reliability-Guaranteed Air-to-Ground Communication for UAVs · IEEE Trans. Mob. Comput. 2024
Vehicular, aerial and satellite networks
UAV-assisted communication
0.812024
Joint Optimization of Mobility and Reliability-Guaranteed Air-to-Ground Communication for UAVs · IEEE Trans. Mob. Comput. 2024
Wireless networking › wireless network performance › wireless network reliability
transmission reliability
0.212024
Joint Optimization of Mobility and Reliability-Guaranteed Air-to-Ground Communication for UAVs · IEEE Trans. Mob. Comput. 2024
Wireless networking › wireless network performance
wireless network performance modeling
0.212024
Joint Optimization of Mobility and Reliability-Guaranteed Air-to-Ground Communication for UAVs · IEEE Trans. Mob. Comput. 2024

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

iterative algorithm · 0.8bilevel optimization · 0.8
YearPublicationVenuePosition
2024 Joint Optimization of Mobility and Reliability-Guaranteed Air-to-Ground Communication for UAVs
abstract
Aerial unmanned vehicles (UAVs) play a significant role in improving the connectivity and coverage of terrestrial communication networks. However, UAV-assisted air-to-ground (A2G) data transmissions usually encounter several fundamental challenges, such as terminal mobility, random nature in channel fading and contention, resource constraints, and application-specific transmission requirements. To tackle these challenges, we formulate a bi-level optimization problem that jointly considers the control of the UAV mobility and transmission power and the scheduling of A2G data transmissions. The objective is to optimize energy consumption and maximize A2G transmission reliability. Particularly, we first theoretically characterize the A2G transmission reliability from a probabilistic perspective concerning the effects of channel fading, channel access contention, and application requirements. We then derive a closed-form expression for the optimal expected transmission reliability. Using the closed-form reliability, we transform the bi-level optimization into a mathematically-tractable optimal control problem and propose an efficient iterative algorithm to solve it. Simulation results show that our approach provides a comprehensive improvement in terms of both energy utilization and A2G transmission reliability, in particular, with a reduction of more than 12.1% in energy consumption and an increase of 7.53% in reliability on average, compared to several baselines.
Jianshan Zhou, Daxin Tian, Yaqing Yan, Xuting Duan, Xuemin Shen
IEEE Trans. Mob. Comput.3