{"paper":{"title":"MAxLM: Multi-Agent Language Model-Based Scheduling and Resource Allocation in MU-MIMO-OFDMA-Enabled Wireless Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"A multi-agent system using pretrained language models optimizes scheduling and resource allocation to increase uplink throughput in MU-MIMO-OFDMA wireless networks.","cross_cats":["cs.MA","cs.NI"],"primary_cat":"eess.SP","authors_text":"Adnan Quadri, Hongxiang Li","submitted_at":"2026-05-15T16:24:40Z","abstract_excerpt":"Wireless networks support multi-user (MU) communication with multiple-input multiple-output (MIMO) and orthogonal frequency-division multiple access (OFDMA) technologies. In the joint MU-MIMO-OFDMA-enabled transmission mode, network throughput can be significantly increased by effectively utilizing the multi-channel resources to schedule numerous wireless users/stations (STAs) simultaneously. In this paper, we study ways to optimize the user scheduling and resource allocation (SRA) for the UL scheduled access (UL-SA) of a joint MU-MIMO-OFDMA-enabled wireless local area network (WLAN). In parti"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Numerical results confirm that our proposed technique achieves higher UL-SA throughput than the benchmark techniques.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The assumption that a general pretrained language model, without domain-specific fine-tuning or explicit mathematical modeling of the wireless channel, can reliably produce near-optimal scheduling decisions across varying numbers of stations and antenna configurations.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A multi-agent language model approach delivers higher uplink throughput than benchmarks for scheduling and resource allocation in MU-MIMO-OFDMA WLANs.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A multi-agent system using pretrained language models optimizes scheduling and resource allocation to increase uplink throughput in MU-MIMO-OFDMA wireless networks.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"febc7c6ab7c9a83afa6ce35d5099149ed7c8b1e585fc57993ec5f05d0aa30a8e"},"source":{"id":"2605.16144","kind":"arxiv","version":1},"verdict":{"id":"fbca9914-c764-492a-8125-a0f9ad66612b","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T21:37:24.551513Z","strongest_claim":"Numerical results confirm that our proposed technique achieves higher UL-SA throughput than the benchmark techniques.","one_line_summary":"A multi-agent language model approach delivers higher uplink throughput than benchmarks for scheduling and resource allocation in MU-MIMO-OFDMA WLANs.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The assumption that a general pretrained language model, without domain-specific fine-tuning or explicit mathematical modeling of the wireless channel, can reliably produce near-optimal scheduling decisions across varying numbers of stations and antenna configurations.","pith_extraction_headline":"A multi-agent system using pretrained language models optimizes scheduling and resource allocation to increase uplink throughput in MU-MIMO-OFDMA wireless networks."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.16144/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_title_agreement","ran_at":"2026-05-19T22:01:23.226684Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T21:51:59.469549Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"cited_work_retraction","ran_at":"2026-05-19T18:22:03.531211Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T17:33:31.047542Z","status":"skipped","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T16:41:55.450740Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"ef6d84f817473c6bc736399db9fed1f8826e0a1e441e00bfaf655a3dff91db19"},"references":{"count":15,"sample":[{"doi":"","year":2021,"title":"R. Zhang, K. Xiong, Y. Lu, B. Gao, P. Fan, and K. B. Letaief, ‘‘Joint coordinated beamforming and power splitting ratio optimization in mu-miso swipt-enabled hetnets: A multi- agent ddqn-based approac","work_id":"6e8b510d-ec1d-4a6d-bf19-26a72512fec5","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2023,"title":"A. Quadri and H. Li, ‘‘Resource allocation using deep learning in uplink 802.11 ax networks,’’ in GLOBECOM 2023-2023 IEEE Global Communications Conference. IEEE, 2023, pp. 4020–4025","work_id":"9b8566f5-be2d-47c6-a07e-feb9b75e3209","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2019,"title":"R. Balakrishnan, K. Sankhe, V. S. Somayazulu, R. Van- nithamby, and J. Sydir, ‘‘Deep reinforcement learning based traffic-and channel-aware ofdma resource allocation,’’ in 2019 IEEE Global Communicatio","work_id":"b762d8b9-5b27-4a70-bcb3-7b5217af9995","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2019,"title":"Y. S. Nasir and D. Guo, ‘‘Multi-agent deep reinforcement learning for dynamic power allocation in wireless networks,’’ IEEE Journal on Selected Areas in Communications, vol. 37, no. 10, pp. 2239–2250,","work_id":"5e555e0d-1737-4cb7-8944-4226236a2397","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2021,"title":"N. Naderializadeh, J. J. Sydir, M. Simsek, and H. Nikopour, ‘‘Resource management in wireless networks via multi-agent deep reinforcement learning,’’ IEEE Transactions on Wireless Communications, vol.","work_id":"e8a4fffc-0929-4c34-9f60-7e3c2141d49a","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":15,"snapshot_sha256":"b97fa0b62766470d60817bdd79446136a0924d65853b420c6cc95c5a10d2b34e","internal_anchors":0},"formal_canon":{"evidence_count":2,"snapshot_sha256":"ce86e8c4f41d51c98d7ddf1616a55d1eca29bdad5d5dcabdd948ba96a2f93b13"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}