Demo Data from EDBT 2026 / reviewers in the wild / expert
Metehan Karatas
dblp:414/0560
· DBLP profile ↗
2ranked-venue papers
2as first author
2since 2021 · last 2026
0000-0003-2669-9668ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 2 · 2 first-author · 2 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 |
Physical-layer communications · 64% Cellular and mobile networks · 36% |
Topics — the 7 heaviest of 7, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Physical-layer communications
beamforming |
0.9 | 1 | 2025 | Iterative Detection Schemes for Uplink Highly Loaded User-Centric Hybrid Beamforming Based Frequency Selective Massive MIMO Networks · IEEE Trans. Commun. 2025 |
Cellular and mobile networks › base station cooperation
cell-free network |
0.9 | 1 | 2025 | Iterative Detection Schemes for Uplink Highly Loaded User-Centric Hybrid Beamforming Based Frequency Selective Massive MIMO Networks · IEEE Trans. Commun. 2025 |
Physical-layer communications › beamforming
hybrid beamforming |
0.9 | 1 | 2025 | Iterative Detection Schemes for Uplink Highly Loaded User-Centric Hybrid Beamforming Based Frequency Selective Massive MIMO Networks · IEEE Trans. Commun. 2025 |
Physical-layer communications › MIMO
massive MIMO |
0.9 | 1 | 2025 | Iterative Detection Schemes for Uplink Highly Loaded User-Centric Hybrid Beamforming Based Frequency Selective Massive MIMO Networks · IEEE Trans. Commun. 2025 |
Cellular and mobile networks › mobile networks › mobile network architecture › cellular network architecture
user-centric network |
0.9 | 1 | 2025 | Iterative Detection Schemes for Uplink Highly Loaded User-Centric Hybrid Beamforming Based Frequency Selective Massive MIMO Networks · IEEE Trans. Commun. 2025 |
Physical-layer communications › equalization
decision feedback equalization |
0.3 | 1 | 2025 | Iterative Detection Schemes for Uplink Highly Loaded User-Centric Hybrid Beamforming Based Frequency Selective Massive MIMO Networks · IEEE Trans. Commun. 2025 |
Physical-layer communications › signal detection
iterative detection |
0.3 | 1 | 2025 | Iterative Detection Schemes for Uplink Highly Loaded User-Centric Hybrid Beamforming Based Frequency Selective Massive MIMO Networks · IEEE Trans. Commun. 2025 |
Methods — techniques the papers use, named apart from their topics
parallel interference cancellation · 0.9iterative block decision feedback equalization · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | VR-VFL: Joint Rate and Client Selection for Vehicular Federated Learning Under Imperfect CSI
Metehan Karatas, Subhrakanti Dey, Christian Rohner, Jose Mairton B. da Silva Jr. |
ICC | 1 |
| 2025 | Iterative Detection Schemes for Uplink Highly Loaded User-Centric Hybrid Beamforming Based Frequency Selective Massive MIMO NetworksabstractIn cell-free (CF) networks, access points (APs) jointly serve the user equipments (UEs). In user-centric (UC) variant, only a handful of APs serve a particular UE, namely the ones with the best channels. In this paper, we propose a hybrid beamforming aided uplink receiver scheme for highly loaded UC massive MIMO (mMIMO) networks, operating in millimeter-wave (mmWave) frequencies, employing single carrier modulation under frequency selective channels. Every AP employs its own analog beamformer (AB) called the modified eigen-beamformer (MEB), which promotes a trend of fairness. In the digital domain, two detectors are proposed; Cell-Free Iterative Detector (CFI-D) and User-Centric Iterative Subset-Detector (UCIS-D). CFI-D introduces the iterative block decision feedback equalization (IB-DFE) method for CF networks. UCIS-D is an alternative to CFI-D. UCIS-D is UC, has low computational complexity (CCP) and is scalable. UCIS-D relies on parallel interference cancellation (PIC) aided IB-DFE. Interfering UEs are divided into three sets. Weak interferers are processed with PIC, strong interferers with IB-DFE which considers the residual interference from PIC. Weakest interferers are ignored. Both PIC and IB-DFE utilize the soft decisions from previous iterations. Simulation results show that these receivers can handle ultra high loads in terms of bit-error-rate (BER) and achievable information rate (AIR). Metehan Karatas, Gökhan Muzaffer Güvensen |
IEEE Trans. Commun. | 1 |