{
  "annotation_id": "37d8ce94-7465-4037-bb76-bcb9809f8467",
  "date_created": "2026-02-17T05:53:11.619000Z",
  "date_modified": "2026-02-17T05:53:11.619000Z",
  "file_hash": "efb64d6ba51e1a6223e3dff309691d767c57c15c7ad8aa3ed4f6e6d2d31ff210",
  "private": false,
  "record": {
    "abstract": "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).",
    "arxiv_id": "2601.07630",
    "authors": [
      "Zihan Jiao",
      "Xinping Yi",
      "Shi Jin"
    ],
    "categories": [
      "eess.SP"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "Learning to Unfold Fractional Programming for Multi-Cell MU-MIMO Beamforming with Graph Neural Networks",
    "url": "https://arxiv.org/abs/2601.07630",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "8286158b-bd84-454c-9575-848939b3d75d",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}