Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.07630 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909987773612032 |
|---|---|
| author | Jiao, Zihan Yi, Xinping Jin, Shi |
| author_facet | Jiao, Zihan Yi, Xinping Jin, Shi |
| contents | In the multi-cell multiuser multi-input multi-output (MU-MIMO) systems, fractional programming (FP) has demonstrated considerable effectiveness in optimizing beamforming vectors, yet it suffers from high computational complexity. Recent improvements demonstrate reduced complexity by avoiding large-dimension matrix inversions (i.e., FastFP) and faster convergence by learning to unfold the FastFP algorithm (i.e., DeepFP). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_07630 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Learning to Unfold Fractional Programming for Multi-Cell MU-MIMO Beamforming with Graph Neural Networks Jiao, Zihan Yi, Xinping Jin, Shi Signal Processing In the multi-cell multiuser multi-input multi-output (MU-MIMO) systems, fractional programming (FP) has demonstrated considerable effectiveness in optimizing beamforming vectors, yet it suffers from high computational complexity. Recent improvements demonstrate reduced complexity by avoiding large-dimension matrix inversions (i.e., FastFP) and faster convergence by learning to unfold the FastFP algorithm (i.e., DeepFP). |
| title | Learning to Unfold Fractional Programming for Multi-Cell MU-MIMO Beamforming with Graph Neural Networks |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2601.07630 |