{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:4SZPXRMTKRB6BN35RZWSZZRMXL","short_pith_number":"pith:4SZPXRMT","schema_version":"1.0","canonical_sha256":"e4b2fbc5935443e0b77d8e6d2ce62cbadad337ebf1d825e8ad8a3ea94d67b726","source":{"kind":"arxiv","id":"2607.15441","version":1},"attestation_state":"computed","paper":{"title":"Low Complexity Neural Network Digital Predistortion of Wideband Power Amplifiers through Feature Selection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Ali H Alsarraf, Cel Thys, Dominique Schreurs, Rodney Martinez Alonso, Sofie Pollin","submitted_at":"2026-07-16T20:25:05Z","abstract_excerpt":"Due to the continuous increase in communication bandwidth and the use of highly efficient yet nonlinear power amplifiers, Digital Predistortion (DPD) algorithms are becoming increasingly complex. In particular, neural network (NN) based DPD approaches using Phase-Normalized NN architectures often incur substantially higher computational costs than widely deployed polynomial-based methods, such as the Memory Polynomial (MP) and Generalized Memory Polynomial (GMP) models. To bridge this gap between research performance and practical implementation, we propose a low-complexity Feature Selection N"},"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":"2607.15441","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SP","submitted_at":"2026-07-16T20:25:05Z","cross_cats_sorted":[],"title_canon_sha256":"23228841536f5a8d9c1684c1564248bfac093cdf32b7189353759563281f4e9c","abstract_canon_sha256":"4b0d44dff824126d9c6eed8ce9ab9f05cec72aa2769d75b481d746a451649fc5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-20T00:18:34.246024Z","signature_b64":"EW1u0BKfQzaxsR0gYzItzuodXRZ+Z0cU9xaKYzRaK3ox4Kjr5DVPgYdh6OsRjIIdS+SHTNG54UAJ2e3+RZFHBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e4b2fbc5935443e0b77d8e6d2ce62cbadad337ebf1d825e8ad8a3ea94d67b726","last_reissued_at":"2026-07-20T00:18:34.245156Z","signature_status":"signed_v1","first_computed_at":"2026-07-20T00:18:34.245156Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Low Complexity Neural Network Digital Predistortion of Wideband Power Amplifiers through Feature Selection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Ali H Alsarraf, Cel Thys, Dominique Schreurs, Rodney Martinez Alonso, Sofie Pollin","submitted_at":"2026-07-16T20:25:05Z","abstract_excerpt":"Due to the continuous increase in communication bandwidth and the use of highly efficient yet nonlinear power amplifiers, Digital Predistortion (DPD) algorithms are becoming increasingly complex. In particular, neural network (NN) based DPD approaches using Phase-Normalized NN architectures often incur substantially higher computational costs than widely deployed polynomial-based methods, such as the Memory Polynomial (MP) and Generalized Memory Polynomial (GMP) models. To bridge this gap between research performance and practical implementation, we propose a low-complexity Feature Selection N"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.15441","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/2607.15441/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":"2607.15441","created_at":"2026-07-20T00:18:34.245611+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.15441v1","created_at":"2026-07-20T00:18:34.245611+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.15441","created_at":"2026-07-20T00:18:34.245611+00:00"},{"alias_kind":"pith_short_12","alias_value":"4SZPXRMTKRB6","created_at":"2026-07-20T00:18:34.245611+00:00"},{"alias_kind":"pith_short_16","alias_value":"4SZPXRMTKRB6BN35","created_at":"2026-07-20T00:18:34.245611+00:00"},{"alias_kind":"pith_short_8","alias_value":"4SZPXRMT","created_at":"2026-07-20T00:18:34.245611+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4SZPXRMTKRB6BN35RZWSZZRMXL","json":"https://pith.science/pith/4SZPXRMTKRB6BN35RZWSZZRMXL.json","graph_json":"https://pith.science/api/pith-number/4SZPXRMTKRB6BN35RZWSZZRMXL/graph.json","events_json":"https://pith.science/api/pith-number/4SZPXRMTKRB6BN35RZWSZZRMXL/events.json","paper":"https://pith.science/paper/4SZPXRMT"},"agent_actions":{"view_html":"https://pith.science/pith/4SZPXRMTKRB6BN35RZWSZZRMXL","download_json":"https://pith.science/pith/4SZPXRMTKRB6BN35RZWSZZRMXL.json","view_paper":"https://pith.science/paper/4SZPXRMT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.15441&json=true","fetch_graph":"https://pith.science/api/pith-number/4SZPXRMTKRB6BN35RZWSZZRMXL/graph.json","fetch_events":"https://pith.science/api/pith-number/4SZPXRMTKRB6BN35RZWSZZRMXL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4SZPXRMTKRB6BN35RZWSZZRMXL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4SZPXRMTKRB6BN35RZWSZZRMXL/action/storage_attestation","attest_author":"https://pith.science/pith/4SZPXRMTKRB6BN35RZWSZZRMXL/action/author_attestation","sign_citation":"https://pith.science/pith/4SZPXRMTKRB6BN35RZWSZZRMXL/action/citation_signature","submit_replication":"https://pith.science/pith/4SZPXRMTKRB6BN35RZWSZZRMXL/action/replication_record"}},"created_at":"2026-07-20T00:18:34.245611+00:00","updated_at":"2026-07-20T00:18:34.245611+00:00"}