{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:QPT5WH4PWDAE5OBMPCCWZNNSV4","short_pith_number":"pith:QPT5WH4P","schema_version":"1.0","canonical_sha256":"83e7db1f8fb0c04eb82c78856cb5b2af394cc45c75252366d753a09e9639af83","source":{"kind":"arxiv","id":"2403.03241","version":2},"attestation_state":"computed","paper":{"title":"NeWRF: A Deep Learning Framework for Wireless Radiation Field Reconstruction and Channel Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NI","authors_text":"Baharan Mirzasoleiman, Christopher Vattheuer, Haofan Lu, Omid Abari","submitted_at":"2024-03-05T18:55:11Z","abstract_excerpt":"We present NeWRF, a deep learning framework for predicting wireless channels. Wireless channel prediction is a long-standing problem in the wireless community and is a key technology for improving the coverage of wireless network deployments. Today, a wireless deployment is evaluated by a site survey which is a cumbersome process requiring an experienced engineer to perform extensive channel measurements. To reduce the cost of site surveys, we develop NeWRF, which is based on recent advances in Neural Radiance Fields (NeRF). NeWRF trains a neural network model with a sparse set of channel meas"},"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":"2403.03241","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2024-03-05T18:55:11Z","cross_cats_sorted":[],"title_canon_sha256":"f9a0e18216fc8cdd372df3f05e6687402300353dc93b4bdc19d9d57fa1724d35","abstract_canon_sha256":"edec9825f852134bfd40b2f9bd2c926dd4449bc929a1544b5f669db4125cb7dd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:32:11.754223Z","signature_b64":"n2nTZR64YjW5PH8InlOj6dTQ+9SpLbWalwnhvEwhzo7vXYfUhavpVHJwhcHgsYLdHMU8CaDcgFjqDn6rlSIkAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"83e7db1f8fb0c04eb82c78856cb5b2af394cc45c75252366d753a09e9639af83","last_reissued_at":"2026-07-05T08:32:11.753715Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:32:11.753715Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"NeWRF: A Deep Learning Framework for Wireless Radiation Field Reconstruction and Channel Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NI","authors_text":"Baharan Mirzasoleiman, Christopher Vattheuer, Haofan Lu, Omid Abari","submitted_at":"2024-03-05T18:55:11Z","abstract_excerpt":"We present NeWRF, a deep learning framework for predicting wireless channels. Wireless channel prediction is a long-standing problem in the wireless community and is a key technology for improving the coverage of wireless network deployments. Today, a wireless deployment is evaluated by a site survey which is a cumbersome process requiring an experienced engineer to perform extensive channel measurements. To reduce the cost of site surveys, we develop NeWRF, which is based on recent advances in Neural Radiance Fields (NeRF). NeWRF trains a neural network model with a sparse set of channel meas"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.03241","kind":"arxiv","version":2},"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/2403.03241/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":"2403.03241","created_at":"2026-07-05T08:32:11.753775+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.03241v2","created_at":"2026-07-05T08:32:11.753775+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.03241","created_at":"2026-07-05T08:32:11.753775+00:00"},{"alias_kind":"pith_short_12","alias_value":"QPT5WH4PWDAE","created_at":"2026-07-05T08:32:11.753775+00:00"},{"alias_kind":"pith_short_16","alias_value":"QPT5WH4PWDAE5OBM","created_at":"2026-07-05T08:32:11.753775+00:00"},{"alias_kind":"pith_short_8","alias_value":"QPT5WH4P","created_at":"2026-07-05T08:32:11.753775+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.04770","citing_title":"WiSER: A Wireless Scene Encoder for Geometry-Grounded Multi-View Wireless Prediction","ref_index":32,"is_internal_anchor":false},{"citing_arxiv_id":"2605.29538","citing_title":"RadioFormer3D: Weakly Supervised 3D Radio Map Estimation in Low-Altitude Airspace via Generative Modeling","ref_index":30,"is_internal_anchor":false},{"citing_arxiv_id":"2604.23310","citing_title":"RadTwin: Generalizable Wireless Digital Twin for Dynamic Environments","ref_index":12,"is_internal_anchor":false},{"citing_arxiv_id":"2604.07086","citing_title":"Radio-Frequency Inverse Rendering for Wireless Environment Modeling","ref_index":19,"is_internal_anchor":false},{"citing_arxiv_id":"2604.16558","citing_title":"Cross-Modal Generation: From Commodity WiFi to High-Fidelity mmWave and RFID Sensing","ref_index":30,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QPT5WH4PWDAE5OBMPCCWZNNSV4","json":"https://pith.science/pith/QPT5WH4PWDAE5OBMPCCWZNNSV4.json","graph_json":"https://pith.science/api/pith-number/QPT5WH4PWDAE5OBMPCCWZNNSV4/graph.json","events_json":"https://pith.science/api/pith-number/QPT5WH4PWDAE5OBMPCCWZNNSV4/events.json","paper":"https://pith.science/paper/QPT5WH4P"},"agent_actions":{"view_html":"https://pith.science/pith/QPT5WH4PWDAE5OBMPCCWZNNSV4","download_json":"https://pith.science/pith/QPT5WH4PWDAE5OBMPCCWZNNSV4.json","view_paper":"https://pith.science/paper/QPT5WH4P","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.03241&json=true","fetch_graph":"https://pith.science/api/pith-number/QPT5WH4PWDAE5OBMPCCWZNNSV4/graph.json","fetch_events":"https://pith.science/api/pith-number/QPT5WH4PWDAE5OBMPCCWZNNSV4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QPT5WH4PWDAE5OBMPCCWZNNSV4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QPT5WH4PWDAE5OBMPCCWZNNSV4/action/storage_attestation","attest_author":"https://pith.science/pith/QPT5WH4PWDAE5OBMPCCWZNNSV4/action/author_attestation","sign_citation":"https://pith.science/pith/QPT5WH4PWDAE5OBMPCCWZNNSV4/action/citation_signature","submit_replication":"https://pith.science/pith/QPT5WH4PWDAE5OBMPCCWZNNSV4/action/replication_record"}},"created_at":"2026-07-05T08:32:11.753775+00:00","updated_at":"2026-07-05T08:32:11.753775+00:00"}