{"paper":{"title":"Learning-based Multiuser Beamforming for Holographic MIMO~Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Shengqian Han, Shiyong Chen","submitted_at":"2025-04-28T06:40:41Z","abstract_excerpt":"Holographic multiple-input multiple-output (HMIMO) can improve spectral efficiency (SE) with low hardware cost, but conventional alternating optimization (AO) methods for jointly optimizing digital and holographic beamformers are computationally expensive. Learning-based beamforming offers a low-complexity alternative, and graph neural networks (GNNs) are particularly attractive because they can exploit permutation equivariance (PE). The optimal HMIMO beamforming policy exhibits PE properties across multiple dimensions. Existing methods either use high-dimensional GNNs, increasing model size a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.19522","kind":"arxiv","version":4},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2504.19522/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}