{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:KEMQ7XYFYQ3FE4V2WQ2GDMXP5D","short_pith_number":"pith:KEMQ7XYF","schema_version":"1.0","canonical_sha256":"51190fdf05c4365272bab43461b2efe8ccfc7073ec817c93bc9647f823513157","source":{"kind":"arxiv","id":"2312.02601","version":1},"attestation_state":"computed","paper":{"title":"A Neural Receiver for 5G NR Multi-user MIMO","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Alexander Keller, Andreas Oeldemann, Andreas Roessler, Fay\\c{c}al A\\\"it Aoudia, Jakob Hoydis, Sebastian Cammerer, Timo Mayer","submitted_at":"2023-12-05T09:19:26Z","abstract_excerpt":"We introduce a neural network (NN)-based multiuser multiple-input multiple-output (MU-MIMO) receiver with 5G New Radio (5G NR) physical uplink shared channel (PUSCH) compatibility. The NN architecture is based on convolution layers to exploit the time and frequency correlation of the channel and a graph neural network (GNN) to handle multiple users. The proposed architecture adapts to an arbitrary number of sub-carriers and supports a varying number of multiple-input multiple-output (MIMO) layers and users without the need for any retraining. The receiver operates on an entire 5G NR slot, i.e."},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2312.02601","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2023-12-05T09:19:26Z","cross_cats_sorted":["math.IT"],"title_canon_sha256":"bd9d5428846be858853d351bed6ad39d51989c2d5d5d2453decc4fc82e2eee11","abstract_canon_sha256":"756ff4d63d2c5cebfa21322c2dffc7f33ecde59da73c65c02413a3ad21a37b43"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:20:20.374119Z","signature_b64":"Ngh6/nTyVDVLF84K49EOryYFODmHRxoF8S4XScQE8c+8axiGtuycHZfQm4XNHCZoe8p8ywrEbO0l0jqIWbFeBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"51190fdf05c4365272bab43461b2efe8ccfc7073ec817c93bc9647f823513157","last_reissued_at":"2026-07-05T07:20:20.373688Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:20:20.373688Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Neural Receiver for 5G NR Multi-user MIMO","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Alexander Keller, Andreas Oeldemann, Andreas Roessler, Fay\\c{c}al A\\\"it Aoudia, Jakob Hoydis, Sebastian Cammerer, Timo Mayer","submitted_at":"2023-12-05T09:19:26Z","abstract_excerpt":"We introduce a neural network (NN)-based multiuser multiple-input multiple-output (MU-MIMO) receiver with 5G New Radio (5G NR) physical uplink shared channel (PUSCH) compatibility. The NN architecture is based on convolution layers to exploit the time and frequency correlation of the channel and a graph neural network (GNN) to handle multiple users. The proposed architecture adapts to an arbitrary number of sub-carriers and supports a varying number of multiple-input multiple-output (MIMO) layers and users without the need for any retraining. The receiver operates on an entire 5G NR slot, i.e."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.02601","kind":"arxiv","version":1},"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/2312.02601/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2312.02601","created_at":"2026-07-05T07:20:20.373749+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.02601v1","created_at":"2026-07-05T07:20:20.373749+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.02601","created_at":"2026-07-05T07:20:20.373749+00:00"},{"alias_kind":"pith_short_12","alias_value":"KEMQ7XYFYQ3F","created_at":"2026-07-05T07:20:20.373749+00:00"},{"alias_kind":"pith_short_16","alias_value":"KEMQ7XYFYQ3FE4V2","created_at":"2026-07-05T07:20:20.373749+00:00"},{"alias_kind":"pith_short_8","alias_value":"KEMQ7XYF","created_at":"2026-07-05T07:20:20.373749+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2608.06230","citing_title":"Neural CRC Prediction for 5G NR URLLC","ref_index":5,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KEMQ7XYFYQ3FE4V2WQ2GDMXP5D","json":"https://pith.science/pith/KEMQ7XYFYQ3FE4V2WQ2GDMXP5D.json","graph_json":"https://pith.science/api/pith-number/KEMQ7XYFYQ3FE4V2WQ2GDMXP5D/graph.json","events_json":"https://pith.science/api/pith-number/KEMQ7XYFYQ3FE4V2WQ2GDMXP5D/events.json","paper":"https://pith.science/paper/KEMQ7XYF"},"agent_actions":{"view_html":"https://pith.science/pith/KEMQ7XYFYQ3FE4V2WQ2GDMXP5D","download_json":"https://pith.science/pith/KEMQ7XYFYQ3FE4V2WQ2GDMXP5D.json","view_paper":"https://pith.science/paper/KEMQ7XYF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.02601&json=true","fetch_graph":"https://pith.science/api/pith-number/KEMQ7XYFYQ3FE4V2WQ2GDMXP5D/graph.json","fetch_events":"https://pith.science/api/pith-number/KEMQ7XYFYQ3FE4V2WQ2GDMXP5D/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KEMQ7XYFYQ3FE4V2WQ2GDMXP5D/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KEMQ7XYFYQ3FE4V2WQ2GDMXP5D/action/storage_attestation","attest_author":"https://pith.science/pith/KEMQ7XYFYQ3FE4V2WQ2GDMXP5D/action/author_attestation","sign_citation":"https://pith.science/pith/KEMQ7XYFYQ3FE4V2WQ2GDMXP5D/action/citation_signature","submit_replication":"https://pith.science/pith/KEMQ7XYFYQ3FE4V2WQ2GDMXP5D/action/replication_record"}},"created_at":"2026-07-05T07:20:20.373749+00:00","updated_at":"2026-07-05T07:20:20.373749+00:00"}