{"as_of":"2026-08-07T19:38:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:63591265534460103b3a7339242bd40f85b0824838d286958360c174f1d91c00","coverage":[{"denominator":53,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":53,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-30T02:22:21.501241Z","state":"measured"},{"denominator":53,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":53,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2606.29345/citation-record","integrity":"/paper/2606.29345/integrity","json":"/paper/2606.29345/citation-record.json","paper":"/paper/2606.29345"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T02:22:21.501241Z","title":"Learning to refine LLRs: Modular neural augmentation for MIMO-OFDM receivers,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:43fd7580406acfb54f740a584141dd75ec125679e65b15a2bf3884d35b109efb","observation_id":"6a73c9a7-c929-4610-88c2-b4a93deafcd5","resolution":{"observed_at":"2026-06-30T02:22:21.501241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T02:22:21.501241Z","title":"On the road to 6G: Visions, requirements, key technologies, and testbeds,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:6e033b233d1e6df866d506c08a72be59ef168aee8d21d39fd72fb18744e3ebec","observation_id":"fb9b06f5-bc1a-4458-bbf7-a4d21b3771e0","resolution":{"observed_at":"2026-06-30T02:22:21.501241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T02:22:21.501241Z","title":"A vision of 6G wireless systems: Applications, trends, technologies, and open research problems,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:4d2b91ef35ec444781e2b7ba383389ee174e3fbcbb272a18fb271e89db8c9a83","observation_id":"90cfd4a6-4efa-4254-b968-c7c7205218b9","resolution":{"observed_at":"2026-06-30T02:22:21.501241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T02:22:21.501241Z","title":"Massive MIMO detection techniques: A survey,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:5555037a66ee5de0686d165023449bfa8c461ac2873a241afee559eed2380e9c","observation_id":"c812ea4f-f2b2-433b-9df4-fa86a9bd1184","resolution":{"observed_at":"2026-06-30T02:22:21.501241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T02:22:21.501241Z","title":"Fifty years of MIMO detection: The road to large-scale MIMOs,","venue":null,"work_id":null,"year":1941},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:a63b40273b12aba722edfec76c32f5dfee8d90349e6a9c4fcbb7af2f79c57bc8","observation_id":"501a9d59-0d92-45cb-a4f2-b2a41899b484","resolution":{"observed_at":"2026-06-30T02:22:21.501241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T02:22:21.501241Z","title":"A sphere decoding algorithm for MIMO channels,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:d3b33cd8301ab13b121a48a26da07cb549bc3ba3ba0834660e807f18f751a4ca","observation_id":"58014aa3-b4bf-4098-ab63-1163ac5c2f80","resolution":{"observed_at":"2026-06-30T02:22:21.501241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T02:22:21.501241Z","title":"Iterative soft interference cancellation for multiple antenna systems,","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:5d3d72c398cb297c4a87d7f7e18c0994098f55926a8aab3f36fc4889bd5d5b9b","observation_id":"badb8c1a-5e02-4631-b0d6-10213e24bead","resolution":{"observed_at":"2026-06-30T02:22:21.501241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T02:22:21.501241Z","title":"RF impairments in wireless transceivers: Phase noise, CFO, and IQ imbalance – A survey,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:f1bc23a4593c45f56618697d6b161eeb1f186848bf84b6ce870c1088534ee6d0","observation_id":"e77ea595-3427-4ff3-aaad-bab0c8a35362","resolution":{"observed_at":"2026-06-30T02:22:21.501241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T02:22:21.501241Z","title":"Channel estimation and performance analysis of one-bit massive MIMO systems,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:a4b0d41f40685928d3e3b162c2d5481022e8db040d7ee4cebfffe33eb489c61b","observation_id":"ee4866c0-8845-4d3b-b1f9-c463c0ec65a6","resolution":{"observed_at":"2026-06-30T02:22:21.501241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T02:22:21.501241Z","title":"Asymptotic task-based quantization with application to massive MIMO,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:0c19afccdb7d8e936f3313ab3080a26e6acf863b6a0ed2f85f0462c43b4a21fd","observation_id":"c228b923-c8e8-44ff-b74a-2faa7d934de3","resolution":{"observed_at":"2026-06-30T02:22:21.501241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T02:22:21.501241Z","title":"Iterative methods for cancellation of intercarrier interference in OFDM systems,","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:ba1e3de9aec5a28244d2b4f7e3333beeaebc0be9d3b16fb9b3d43bbd4f6b6bb7","observation_id":"0e4c2c4d-ccb3-4546-ac49-be4f29197352","resolution":{"observed_at":"2026-06-30T02:22:21.501241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T02:22:21.501241Z","title":"Digital postcompensation using volterra series transfer function,","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:ac647315b99a2e1e10a987ae9a4092b1ba252b7883eae4445eca411a1c29eab9","observation_id":"5a6ca796-516e-40bc-837b-74bd5cdcc025","resolution":{"observed_at":"2026-06-30T02:22:21.501241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T02:22:21.501241Z","title":"Deep learning for wireless communications: An emerging interdisciplinary paradigm,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:095949cc34bbf60058b2c722563d0c0098ade966aca6a552a0dbc9c776794cc1","observation_id":"bd5775b6-f4d7-46f4-b7a5-a80485deffd2","resolution":{"observed_at":"2026-06-30T02:22:21.501241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T02:22:21.501241Z","title":"Neural network detection of data sequences in communication systems,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:bcc940534ee0a37365a62bb9fc72b5f866f14a38987a8775de8c395d56ae15d9","observation_id":"9820f8e0-1f93-44bf-89d1-41ba01cef17d","resolution":{"observed_at":"2026-06-30T02:22:21.501241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T02:22:21.501241Z","title":"Machine learning for MU-MIMO receive processing in OFDM systems,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:441880760a85218a4a2376215588675530ac61a534cc58bff49240f421a7d279","observation_id":"69ff14f5-01c3-41f2-9682-19ef1e4a79d3","resolution":{"observed_at":"2026-06-30T02:22:21.501241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T02:22:21.501241Z","title":"A deep learning receiver for non-linear transmitter,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:dc2824ddb572408533f2a74e134806827587c2a9bb8371ed325b64175eb3b9ee","observation_id":"9dc4c42a-a15a-449b-80b2-2984d8a9dfa5","resolution":{"observed_at":"2026-06-30T02:22:21.501241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T02:22:21.501241Z","title":"DeepRx: Fully convolutional deep learning receiver,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:1241ec71e5e7df44b12ffb549ecc2fed2e7aacc5c6e7d4500c830a3dadf9beef","observation_id":"36440237-3f35-4c7d-af43-deb18fdec802","resolution":{"observed_at":"2026-06-30T02:22:21.501241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T02:22:21.501241Z","title":"Deep learning OFDM receivers for improved power efficiency and coverage,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:d77a30819b005509f8ac6cccf01d6741c38ed0b5bf4a5b97eba7e0e809bcd964","observation_id":"2bdf6190-311d-4e37-8919-1286bfed8549","resolution":{"observed_at":"2026-06-30T02:22:21.501241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T02:22:21.501241Z","title":"Deep learning based OFDM physical-layer receiver with multidilated convolutions,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:a27efc0763fdcd8b493f26317e6572c55fb13ffd7eb510a1a3974378093ff72b","observation_id":"487c23de-66a7-4aac-8bde-80e3e2ac614c","resolution":{"observed_at":"2026-06-30T02:22:21.501241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T02:22:21.501241Z","title":"Joint detection and decoding: A graph neural network approach,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:301ea345ee7badb81786004dc59c4bfbe8a9ef98d99a3fc1adebd42a9ae17cf0","observation_id":"d52f1f5a-7fbe-45d4-acc9-ea4dccd57e2a","resolution":{"observed_at":"2026-06-30T02:22:21.501241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T02:22:21.501241Z","title":"Graph Neural Networks-Enhanced expectation propagation algorithm for MIMO turbo receiver,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:f345e5091beb8363e45b32d1a7b53c5e33408b70377df3b14fa92f78038dbc5d","observation_id":"ac6b5032-b591-4ab2-9c52-d241df482f2d","resolution":{"observed_at":"2026-06-30T02:22:21.501241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T02:22:21.501241Z","title":"Hypernetwork- enhanced GEPNet for MIMO-OFDM receiver with imperfect CSI,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:9101dccd5a130ef2e156a2360983e63e20cd4163499b258fd5aa78c38d17d7e3","observation_id":"a11b3bf6-7db9-45cb-bddc-9646fe40e7f0","resolution":{"observed_at":"2026-06-30T02:22:21.501241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T02:22:21.501241Z","title":"A neural receiver for 5G NR multi-user MIMO,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:34bfb7c499a466634a546ccf939347d8b330cba1d3cca05dda97f7af899654db","observation_id":"479c7f7a-a840-405b-b2a2-cd1a3c72aeab","resolution":{"observed_at":"2026-06-30T02:22:21.501241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T02:22:21.501241Z","title":"Unsupervised linear and nonlinear channel equalization and decoding using variational autoencoders,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:0d5c09215bb42c47e5761d28f0c5780bb7dd361f175febd39dbbfe5569ca1c2b","observation_id":"62d9e105-9920-407e-be23-7b6c2e880efb","resolution":{"observed_at":"2026-06-30T02:22:21.501241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T02:22:21.501241Z","title":"Comm-transformer: A robust deep learning-based receiver for OFDM system under TDL chan- nel,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:9dc5e06c93dc9dd0ec761910cbfa1a8542dfcad67d51e8d63625df5445613b4c","observation_id":"3c91a990-3edf-4b3e-b5cd-ffff9ef84839","resolution":{"observed_at":"2026-06-30T02:22:21.501241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T02:22:21.501241Z","title":"Convolutional self-attention-based multi-user MIMO demapper,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:3fbd1f1f17326e9de747b84f55d85e06a24a8c8982d83b5d8596b02bd1a6b5f4","observation_id":"e74aeeb0-c9c3-4d32-9e54-03e8f4ca7c4f","resolution":{"observed_at":"2026-06-30T02:22:21.501241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T02:22:21.501241Z","title":"Deep learning in wireless communication receivers: A survey,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:400d41f4c679f37d51cf5cf80166eedbca0a19916ec52ec63c7655a68d1486e7","observation_id":"f859d0b3-6114-4332-b505-c93c2000d96a","resolution":{"observed_at":"2026-06-30T02:22:21.501241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T02:22:21.501241Z","title":"Design of a standard-compliant real-time neural receiver for 5G NR,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:de9ec5e6a7b8009f8733ae94f93046c6a30dce59c65a995c416902beb2babe2c","observation_id":"4bfae09b-079d-46e5-a7db-ef90279c88f4","resolution":{"observed_at":"2026-06-30T02:22:21.501241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T02:22:21.501241Z","title":"Adaptive and flexible model-based AI for deep receivers in dynamic channels,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:9e000b02dc642b31f31c7d3d1966a56bc337e523663143a270b304e27971324d","observation_id":"2fc87c2c-7fc4-47b1-930f-fe61606a361b","resolution":{"observed_at":"2026-06-30T02:22:21.501241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T02:22:21.501241Z","title":"Model-based deep learning,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:e8ac75e77b33857c386281957658cf62a39a87af9f646bba159d7f1cfa72b759","observation_id":"cfcea480-ad9c-45fb-96b1-f2ec583ed2ff","resolution":{"observed_at":"2026-06-30T02:22:21.501241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T02:22:21.501241Z","title":"Comprehensive review of deep unfolding techniques for next-generation wireless communication systems,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:c61aa33eecb29cf33fdd280c3c618b93b8f550944ee8408db2abb41aa701aef4","observation_id":"8347586d-b4a9-49de-8e84-909c1dd4b8cb","resolution":{"observed_at":"2026-06-30T02:22:21.501241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1906.05774","last_updated":"2019-10-08T14:44:59Z","snapshot_observed_at":"2026-08-04T08:34:08.429121Z","submitted_at":"2019-06-13T16:02:52Z","title":"Deep Unfolding for Communications Systems: A Survey and Some New Directions","version":2},"cited_work":{"arxiv_id":"1906.05774","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1906.05774","snapshot_observed_at":"2026-06-30T02:24:12.980379Z","title":"Deep unfolding for communications systems: A survey and some new directions,","venue":null,"work_id":"3fc6e7ed-4a45-4d24-b17c-4091e360fd9e","year":1906},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"cited_paper":"/paper/1906.05774","citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:2be1c9511dcf35ad7ea82f64d2c6d6184e6af6681387a165c02b2b1f2f599d44","observation_id":"af61a6be-d611-4d17-ba86-8bd4e507f31e","resolution":{"observed_at":"2026-06-30T02:24:12.982017Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2512.03768","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-03T10:58:03.426487Z","title":"Deep unfolding: Recent developments, theory, and design guidelines","venue":null,"work_id":"f67fb403-b24c-46e0-957e-5a7329df9706","year":2025},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:2b99d526e8918206f2c8225d3a3f5a803e847b5c60dad93d2ceccae4f2fcc7a9","observation_id":"cd71c3d3-e8e4-4347-8e10-e73c793c81f8","resolution":{"observed_at":"2026-06-30T02:24:12.976011Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T02:22:21.501241Z","title":"A comprehensive survey of knowledge-driven deep learning for intelligent wireless network optimization in 6G,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:807542ecbeb75f586d33c5ce79558ff81286b541f4b22693724d018af78dbb63","observation_id":"324a72ed-65c4-4a85-b487-2b7155f38fd2","resolution":{"observed_at":"2026-06-30T02:22:21.501241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2602.11834","last_updated":"2026-08-06T09:16:24Z","snapshot_observed_at":"2026-08-07T19:26:31.092613Z","submitted_at":"2026-02-12T11:22:30Z","title":"EqDeepRx: Learning a Scalable and Interference Mitigating MIMO Receiver","version":2},"cited_work":{"arxiv_id":"2602.11834","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2602.11834","snapshot_observed_at":"2026-08-07T01:36:13.958205Z","title":"EqDeepRx: Learning a scalable MIMO receiver,","venue":null,"work_id":"73e8c3da-c680-4a18-ac7c-3cbfe8a948e9","year":2026},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"cited_paper":"/paper/2602.11834","citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:67c00c7d54aefe380fbafda9816787f3932ae53ea8866653dd34dcd1712dd9c7","observation_id":"a847720d-ef88-4f4d-941c-5b0cce499dbc","resolution":{"observed_at":"2026-08-07T01:36:13.958205Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T02:22:21.501241Z","title":"Deep learning- based low complexity MIMO detection via partial MAP,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:96e2f02c5a8ef1c878735a32717efc8b81d42585f94b7682fee33390a4ceab4d","observation_id":"c3111566-53b3-4b72-b632-60f10ffce9e9","resolution":{"observed_at":"2026-06-30T02:22:21.501241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2601.16586","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T02:24:12.977325Z","title":"Learning successive interference cancellation for low-complexity soft-output MIMO detection,","venue":null,"work_id":"30be2187-efc4-4125-88b0-3afcf415547d","year":2026},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:f7b64390de070bb93dd079ad4860a77e88f23e31c375801ba0b434e8022d8411","observation_id":"782db6bd-3b06-4d53-92d7-9405800d0cfc","resolution":{"observed_at":"2026-06-30T02:24:12.979139Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T02:22:21.501241Z","title":"“Machine LLRning","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:178c7bc53e857278419444d135c409878ad895c5057f48b046cf941e851e4947","observation_id":"bc3d1250-ac65-482f-8678-7edc567e90fc","resolution":{"observed_at":"2026-06-30T02:22:21.501241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T02:22:21.501241Z","title":"Adaptive neural signal detection for massive MIMO,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:ac0cc9ef6754e3507441f3ffe1530112806bf47ceab214508dd82ac9eff673aa","observation_id":"9c6b292f-f7cc-4e95-adae-726ebe1b662e","resolution":{"observed_at":"2026-06-30T02:22:21.501241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T02:22:21.501241Z","title":"Model-driven deep learning for MIMO detection,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:c9d1a8ce24ed1e0098c90dd9b4fb299471fdfe6cfc00dd19700a998a05330e32","observation_id":"e3a309d2-76c9-40a2-9f79-4a65a39be9e4","resolution":{"observed_at":"2026-06-30T02:22:21.501241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T02:22:21.501241Z","title":"GNN-assisted BiG-AMP: Joint channel estimation and data detection for massive MIMO receiver,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:22aa9e9c3042e7d7b86e6eccdb2ab52713bf4e035918a05e06ceafa1c6cb0649","observation_id":"7afc00a8-bd6f-478a-9e40-200a6670675c","resolution":{"observed_at":"2026-06-30T02:22:21.501241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T02:22:21.501241Z","title":"DeepSIC: Deep soft interference cancellation for multiuser MIMO detection,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:93a7e503b1fb5f942b74dcfd45d5ba919866f89d55203f810203260e3254b7ba","observation_id":"2b81c50c-a00f-4aee-8226-733e0f95b6f1","resolution":{"observed_at":"2026-06-30T02:22:21.501241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T02:22:21.501241Z","title":"Deep learning based successive interference cancellation for the non-orthogonal downlink,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:30d247e9e42048b594b46557ee7e5b16d4f12d9b0d66a7b4c7abda8972dc062a","observation_id":"781340c0-2c83-430e-bbb0-5125ae45a948","resolution":{"observed_at":"2026-06-30T02:22:21.501241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T02:22:21.501241Z","title":"Model-based deep learning receiver design for rate-splitting multiple access,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:6330bc05b3557aa12673049c6cbc912a009d7a603a38ce52077807d921341274","observation_id":"aee72f9e-37b1-4f6d-a7a0-74e58a8f3351","resolution":{"observed_at":"2026-06-30T02:22:21.501241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T02:22:21.501241Z","title":"Study on channel model for frequencies from 0.5 to 100 GHz,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:566ea511c0f38a52f3666d3c972efc28c1b05327acf27b564438a551f1241f58","observation_id":"0dbfeb23-8107-4440-b2c0-08d025aa0205","resolution":{"observed_at":"2026-06-30T02:22:21.501241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T02:22:21.501241Z","title":"Hoydis, S","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:3eddc028ac3849ac22e7e82ec9cc0feb72b241d5488be621a4f096dae8e8b952","observation_id":"c3b3a1df-4fea-4e44-ab38-b21eabbfeb2b","resolution":{"observed_at":"2026-06-30T02:22:21.501241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T02:22:21.501241Z","title":"Quadriga: A 3-D multi-cell channel model with time evolution for enabling virtual field trials,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:93d68a1481730aa53fb616fc157aaccb19e67bf61f42e8cf8110c52f40a6a233","observation_id":"e1a4ca11-c0b8-4dba-8e5c-e6645e71656a","resolution":{"observed_at":"2026-06-30T02:22:21.501241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T02:22:21.501241Z","title":"Joint estimation of carrier frequency offset and channel impulse response for linear periodic channels,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:69e33f9b76027848095e3c32845a4ef680008e4c8c71294ea755438853ce2a77","observation_id":"45309ca3-2603-4fec-85c4-ff08d96397e4","resolution":{"observed_at":"2026-06-30T02:22:21.501241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T02:22:21.501241Z","title":"Asynchronous online adaptation via modular drift detection for deep receivers,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:5c5b311ccb82c2f6eef14674f3e8055573bc8563eb287e3256e9bee0817b6d6b","observation_id":"b8e536e5-8af7-44b9-9f93-1b0336df0625","resolution":{"observed_at":"2026-06-30T02:22:21.501241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T02:22:21.501241Z","title":"Algorithm unrolling: Inter- pretable, efficient deep learning for signal and image processing,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:9139e9b184291addf0ab9f6bf4d44a3ce82398cc8a48dea4181b73a607de89bf","observation_id":"fef81647-e34c-4e63-aa3d-50993d98e574","resolution":{"observed_at":"2026-06-30T02:22:21.501241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T02:22:21.501241Z","title":"Online learning of modular bayesian deep receivers: Single-step adaptation with streaming data,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:9ee528f551c0f1c19ca3ba4ca8c18afb3e86511bcc8dd7a39faf8a2865afc4b4","observation_id":"ba46b7ef-a160-4610-9443-a6c38255fee3","resolution":{"observed_at":"2026-06-30T02:22:21.501241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T02:22:21.501241Z","title":"Modular hypernetworks for scalable and adaptive deep MIMO receivers,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:0499e80b524b8700006433f2c633db8ca4743f821fc6a9f585d338ff743dc025","observation_id":"b89c9bba-cdaf-4a4a-bc57-2695369447bd","resolution":{"observed_at":"2026-06-30T02:22:21.501241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-30T02:22:21.501241Z","title":"Performance assessment of MIMO-BICM demodulators based on mutual information,","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-06-30T02:22:21.501241Z"},"links":{"citing_paper":"/paper/2606.29345"},"observation_digest":"sha256:fae042d05fffc35e14ce8f8652aa4f3d38ecdb1735777d2a8336e0f26396c1bd","observation_id":"7ffa15b4-8c6d-47d5-9329-f249731d45a8","resolution":{"observed_at":"2026-06-30T02:22:21.501241Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2606.29345","last_updated":"2026-06-28T11:22:11Z","latest_version":1,"primary_category":"eess.SP","snapshot_observed_at":"2026-08-07T00:05:35.546631Z","submitted_at":"2026-06-28T11:22:11Z","title":"Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction"},"reference_resolution":{"displayed":53,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":49,"verified_exact":4,"verified_fuzzy":0},"total_outbound_references":53},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 0 inbound Pith citation observations for arXiv:2606.29345."}