Fumiyuki Adachi
Electrical and Electronic Engineering · 2015–2020
A researcher in the Science corpus: 5 papers; trusts 10 researchers and is trusted by 10 researchers here, as inferred from the citation graph.
Subfields
- Electrical and Electronic Engineering — 76 citations into it; #3,204 of 16,539 by trust here, #7,175 by citations
- Computer Networks and Communications — 42 citations into it
Whom Fumiyuki Adachi trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
Who trusts Fumiyuki Adachi overall
- Zhiguo Ding
- Yuanwei Liu
- Xidong Mu
- Zhengju Yang
- S. M. Riazul Islam
- Muhammad Basit Shahab
- Muhammad Fainan Hanif
- Robert Schober
- Ming Zeng
Papers from Fumiyuki Adachi's perspective
Ordered by how strongly the researchers Fumiyuki Adachi trusts endorse each paper. Same corpus, a different order for every researcher.
- Millimeter Wave Mobile Communications for 5G Cellular: It Will Work!
- An Introduction to Deep Learning for the Physical Layer
- Spatially Sparse Precoding in Millimeter Wave MIMO Systems (IEEE Transactions on Wireless Communications)
- Five disruptive technology directions for 5G
- Millimeter-Wave Enhanced Local Area Systems: A High-Data-Rate Approach for Future Wireless Networks (IEEE Journal on Selected Areas in Communications)
- Wireless communications with unmanned aerial vehicles: opportunities and challenges (IEEE Communications Magazine)
- A Fast Learning Algorithm for Deep Belief Nets
- Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems (IEEE Wireless Communications Letters)
- A survey on 3GPP heterogeneous networks (IEEE wireless communications)
- Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications
Their papers
- The Application of MIMO to Non-Orthogonal Multiple Access (2015) — 780 citations
- 6G: Opening New Horizons for Integration of Comfort, Security, and Intelligence (2020) — 571 citations
- Deep-Learning-Based Millimeter-Wave Massive MIMO for Hybrid Precoding (2019) — 424 citations
- A Survey on Resource Allocation for 5G Heterogeneous Networks: Current Research, Future Trends, and Challenges (2020) — 555 citations
- Deep Learning for Physical-Layer 5G Wireless Techniques: Opportunities, Challenges and Solutions (2019) — 386 citations