{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:4CGGDV5UYY7266HYFZGF5KO7IP","short_pith_number":"pith:4CGGDV5U","schema_version":"1.0","canonical_sha256":"e08c61d7b4c63faf78f82e4c5ea9df43f1b4447d51fba5c73db7fe4172f47fc2","source":{"kind":"arxiv","id":"2312.01546","version":1},"attestation_state":"computed","paper":{"title":"Learning Channel Capacity with Neural Mutual Information Estimator Based on Message Importance Measure","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.SP","math.IT"],"primary_cat":"cs.IT","authors_text":"Chenghui Peng, Khaled B. Letaief, Pingyi Fan, Rui She, Zhefan Li","submitted_at":"2023-12-04T00:26:00Z","abstract_excerpt":"Channel capacity estimation plays a crucial role in beyond 5G intelligent communications. Despite its significance, this task is challenging for a majority of channels, especially for the complex channels not modeled as the well-known typical ones. Recently, neural networks have been used in mutual information estimation and optimization. They are particularly considered as efficient tools for learning channel capacity. In this paper, we propose a cooperative framework to simultaneously estimate channel capacity and design the optimal codebook. First, we will leverage MIM-based GAN, a novel fo"},"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":"2312.01546","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2023-12-04T00:26:00Z","cross_cats_sorted":["eess.SP","math.IT"],"title_canon_sha256":"58aa24aae604fb321b1f8bbfe7eb7418c71f3bcdef07c9022f89d0d67430770d","abstract_canon_sha256":"7250c4b47161f8d987dc6aea942e326c519a266dd8a9cb64ab7fcf99c2dc4ab7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:19:56.423070Z","signature_b64":"XbmdRNe6JF6opZi0674HjpfXbm45IYHWRdFhKQElOSWEW98lDzQSPknhN01S0v7Afe+PxygRGnLjBXdOIdBPCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e08c61d7b4c63faf78f82e4c5ea9df43f1b4447d51fba5c73db7fe4172f47fc2","last_reissued_at":"2026-07-05T07:19:56.422630Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:19:56.422630Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Channel Capacity with Neural Mutual Information Estimator Based on Message Importance Measure","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.SP","math.IT"],"primary_cat":"cs.IT","authors_text":"Chenghui Peng, Khaled B. Letaief, Pingyi Fan, Rui She, Zhefan Li","submitted_at":"2023-12-04T00:26:00Z","abstract_excerpt":"Channel capacity estimation plays a crucial role in beyond 5G intelligent communications. Despite its significance, this task is challenging for a majority of channels, especially for the complex channels not modeled as the well-known typical ones. Recently, neural networks have been used in mutual information estimation and optimization. They are particularly considered as efficient tools for learning channel capacity. In this paper, we propose a cooperative framework to simultaneously estimate channel capacity and design the optimal codebook. First, we will leverage MIM-based GAN, a novel fo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.01546","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/2312.01546/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":"2312.01546","created_at":"2026-07-05T07:19:56.422686+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.01546v1","created_at":"2026-07-05T07:19:56.422686+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.01546","created_at":"2026-07-05T07:19:56.422686+00:00"},{"alias_kind":"pith_short_12","alias_value":"4CGGDV5UYY72","created_at":"2026-07-05T07:19:56.422686+00:00"},{"alias_kind":"pith_short_16","alias_value":"4CGGDV5UYY7266HY","created_at":"2026-07-05T07:19:56.422686+00:00"},{"alias_kind":"pith_short_8","alias_value":"4CGGDV5U","created_at":"2026-07-05T07:19:56.422686+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/4CGGDV5UYY7266HYFZGF5KO7IP","json":"https://pith.science/pith/4CGGDV5UYY7266HYFZGF5KO7IP.json","graph_json":"https://pith.science/api/pith-number/4CGGDV5UYY7266HYFZGF5KO7IP/graph.json","events_json":"https://pith.science/api/pith-number/4CGGDV5UYY7266HYFZGF5KO7IP/events.json","paper":"https://pith.science/paper/4CGGDV5U"},"agent_actions":{"view_html":"https://pith.science/pith/4CGGDV5UYY7266HYFZGF5KO7IP","download_json":"https://pith.science/pith/4CGGDV5UYY7266HYFZGF5KO7IP.json","view_paper":"https://pith.science/paper/4CGGDV5U","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.01546&json=true","fetch_graph":"https://pith.science/api/pith-number/4CGGDV5UYY7266HYFZGF5KO7IP/graph.json","fetch_events":"https://pith.science/api/pith-number/4CGGDV5UYY7266HYFZGF5KO7IP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4CGGDV5UYY7266HYFZGF5KO7IP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4CGGDV5UYY7266HYFZGF5KO7IP/action/storage_attestation","attest_author":"https://pith.science/pith/4CGGDV5UYY7266HYFZGF5KO7IP/action/author_attestation","sign_citation":"https://pith.science/pith/4CGGDV5UYY7266HYFZGF5KO7IP/action/citation_signature","submit_replication":"https://pith.science/pith/4CGGDV5UYY7266HYFZGF5KO7IP/action/replication_record"}},"created_at":"2026-07-05T07:19:56.422686+00:00","updated_at":"2026-07-05T07:19:56.422686+00:00"}