Chenyang Yang
Chinese professor (ORCID 0000-0003-0058-0765)
Facts
9 properties- Instance of
- human
- Country of citizenship
- People's Republic of China[2]
- Occupation
- researcher
- Employer
- Beihang University (1999–present)[3]
- Educated at
- Beihang University (?–1997)[4]
- Imported from
- wholeness_audit_2026-05-02
🏷️ Also known as
en Chenyang Yang
ko 양천양
zh 杨晨阳
🔖 Identifiers · 5
- ORCID iD
- 0000-0003-0058-0765
- OpenAlex ID
- A5082950698, A5079726858, A5135980880as of 2026-09-23
where from?
- Source
- OpenAlex
- Edition
- 20260923 · as of 2026-09-23
- Licence
- CC0 1.0 Universal
- Bronze
- not in the catalog
- Transform
- 4ort-mint · openalex:20260923 · version not recorded
- DBLP author ID
- 32/2760
- IEEE Xplore author ID
- 37279099600
- LinkedIn personal profile ID
- chenyang-yang-7980304
📚 Sources · 1
📜 Data licence
Source: OpenAlex (openalex.org), snapshot of 2026-09-23. CC0 1.0 Universal. CC0 1.0 Universal · OpenAlex
🔗 Connections
Author of 37
Accelerating Deep Reinforcement Learning With the Aid of Partial Model: Energy-Efficient Predictive Video Streaming,
A Deep Reinforcement Learning Approach to Proactive Content Pushing and Recommendation for Mobile Users,
A Learning-Based Approach to Joint Content Caching and Recommendation at Base Stations,
A Model-Based GNN for Learning Precoding,
A Tutorial on Ultrareliable and Low-Latency Communications in 6G: Integrating Domain Knowledge Into Deep Learning,
Caching Policy Optimization for D2D Communications by Learning User Preference,
Caching Policy Toward Maximal Success Probability and Area Spectral Efficiency of Cache-Enabled HetNets,
Data-driven resource allocation with traffic load prediction
+29 more
← Authored by 37
Accelerating Deep Reinforcement Learning With the Aid of Partial Model: Energy-Efficient Predictive Video Streaming,
A Deep Reinforcement Learning Approach to Proactive Content Pushing and Recommendation for Mobile Users,
A Learning-Based Approach to Joint Content Caching and Recommendation at Base Stations,
A Model-Based GNN for Learning Precoding,
A Tutorial on Ultrareliable and Low-Latency Communications in 6G: Integrating Domain Knowledge Into Deep Learning,
Caching Policy Optimization for D2D Communications by Learning User Preference,
Caching Policy Toward Maximal Success Probability and Area Spectral Efficiency of Cache-Enabled HetNets,
Data-driven resource allocation with traffic load prediction
+29 more
Affiliated with 1
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