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Lizhou Zhou

dblp:272/9906 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none

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

Artificial intelligence and machine learning · 1Databases, data management, data science and information retrieval · 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
1 paper
Physical-layer communications · 77% Cellular and mobile networks · 23%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Physical-layer communications
radio propagation
0.412020
Cellular Network Radio Propagation Modeling with Deep Convolutional Neural Networks · KDD 2020
Cellular and mobile networks
network planning and optimization
0.112020
Cellular Network Radio Propagation Modeling with Deep Convolutional Neural Networks · KDD 2020

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

ray tracing · 0.4convolutional neural network · 0.4
YearPublicationVenuePosition
2020 Cellular Network Radio Propagation Modeling with Deep Convolutional Neural Networks
abstract
Radio propagation modeling and prediction is fundamental for modern cellular network planning and optimization. Conventional radio propagation models fall into two categories. Empirical models, based on coarse statistics, are simple and computationally efficient, but are inaccurate due to oversimplification. Deterministic models, such as ray tracing based on physical laws of wave propagation, are more accurate and site specific. But they have higher computational complexity and are inflexible to utilize site information other than traditional global information system (GIS) maps.
Xiujun Shu, Bingwen Zhang, Jie Ren 0013, Lizhou Zhou, Xin Chen 0062
KDD5