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1 | 1 | # DO NOT EDIT, GENERATED AUTOMATICALLY |
2 | 2 |
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3 | | -- id: doi:10.48550/ARXIV.2506.03167 |
4 | | - title: Distributionally Robust Wireless Semantic Communication with Large AI Models |
5 | | - authors: |
6 | | - - Long Tan Le |
7 | | - - Senura Hansaja Wanasekara |
8 | | - - Zerun Niu |
9 | | - - Yansong Shi |
10 | | - - Nguyen H. Tran |
11 | | - - Phuong Vo |
12 | | - - Walid Saad |
13 | | - - Dusit Niyato |
14 | | - - Zhu Han |
15 | | - - Choong Seon Hong |
16 | | - - H. Vincent Poor |
17 | | - publisher: arXiv |
18 | | - date: '2024-01-01' |
19 | | - link: https://doi.org/g9v3k4 |
20 | | - orcid: 0009-0004-1110-737X |
21 | | - plugin: sources.py |
22 | | - file: sources.yaml |
23 | | - type: paper |
24 | | - description: A distributionally robust approach for wireless semantic communication |
25 | | - with large AI models that addresses uncertainty in wireless channels and semantic |
26 | | - information transmission, enhancing reliability in AI-powered communication systems. |
27 | | - buttons: |
28 | | - - type: source |
29 | | - text: ArXiv |
30 | | - link: https://arxiv.org/abs/2506.03167 |
31 | | - tags: |
32 | | - - semantic communication |
33 | | - - large AI models |
34 | | - - distributionally robust optimization |
35 | | - - wireless communication |
36 | | - - uncertainty |
37 | | - - robustness |
38 | | - journal: arXiv preprint |
39 | | -- id: doi:10.1109/atc63255.2024.10908307 |
40 | | - title: Lossy Compression of Multi-Channel EEG and PPG Signals Based on Golomb-Rice |
41 | | - Coding with Parameter Estimation |
42 | | - authors: |
43 | | - - Senura Hansaja Wanasekara |
44 | | - - Han Huy Dung |
45 | | - - Ngoc Hung Nguyen |
46 | | - - Van-Dinh Nguyen |
47 | | - publisher: 2024 International Conference on Advanced Technologies for Communications |
48 | | - (ATC) |
49 | | - date: '2024-10-17' |
50 | | - link: https://doi.org/g9wbrw |
51 | | - orcid: 0009-0004-1110-737X |
52 | | - plugin: orcid.py |
53 | | - file: orcid.yaml |
54 | 3 | - id: arxiv:2006.08848 |
55 | 4 | title: Personalized Federated Learning with Moreau Envelopes |
56 | 5 | authors: |
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255 | 204 | journal: Mobile Networks and Applications |
256 | 205 | plugin: sources.py |
257 | 206 | file: sources.yaml |
| 207 | +- id: doi:10.48550/ARXIV.2506.03167 |
| 208 | + title: Distributionally Robust Wireless Semantic Communication with Large AI Models |
| 209 | + authors: |
| 210 | + - Long Tan Le |
| 211 | + - Senura Hansaja Wanasekara |
| 212 | + - Zerun Niu |
| 213 | + - Yansong Shi |
| 214 | + - Nguyen H. Tran |
| 215 | + - Phuong Vo |
| 216 | + - Walid Saad |
| 217 | + - Dusit Niyato |
| 218 | + - Zhu Han |
| 219 | + - Choong Seon Hong |
| 220 | + - H. Vincent Poor |
| 221 | + publisher: arXiv |
| 222 | + date: '2024-01-01' |
| 223 | + link: https://doi.org/g9v3k4 |
| 224 | + type: paper |
| 225 | + description: A distributionally robust approach for wireless semantic communication |
| 226 | + with large AI models that addresses uncertainty in wireless channels and semantic |
| 227 | + information transmission, enhancing reliability in AI-powered communication systems. |
| 228 | + buttons: |
| 229 | + - type: source |
| 230 | + text: ArXiv |
| 231 | + link: https://arxiv.org/abs/2506.03167 |
| 232 | + tags: |
| 233 | + - semantic communication |
| 234 | + - large AI models |
| 235 | + - distributionally robust optimization |
| 236 | + - wireless communication |
| 237 | + - uncertainty |
| 238 | + - robustness |
| 239 | + journal: arXiv preprint |
| 240 | + plugin: sources.py |
| 241 | + file: sources.yaml |
258 | 242 | - id: arxiv:2407.07421 |
259 | 243 | title: Federated PCA on Grassmann Manifold for IoT Anomaly Detection |
260 | 244 | authors: |
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