{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:WMY5IIWDUDA6MQNG4ZY3SI34NA","short_pith_number":"pith:WMY5IIWD","schema_version":"1.0","canonical_sha256":"b331d422c3a0c1e641a6e671b9237c680c681a981d01e7347361335597d37553","source":{"kind":"arxiv","id":"1812.02986","version":1},"attestation_state":"computed","paper":{"title":"Channel Tracking for Wireless Energy Transfer: A Deep Recurrent Neural Network Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Chang-Jae Chun, Dong In Kim, Il-Min Kim, Jae-Mo Kang","submitted_at":"2018-12-07T11:32:08Z","abstract_excerpt":"In this paper, we study channel tracking for the wireless energy transfer (WET) system, which is practically a very important, but challenging problem. Regarding the time-varying channels as a sequence to be predicted, we exploit the recurrent neural network (RNN) technique for channel tracking. Particularly, combining the deep long short-term memory (LSTM) RNN with the deep feedforward neural network, we develop a novel channel tracking scheme for the WET system, which estimates the channel state information (CSI) at the energy transmitter based on the previous CSI estimates, and the current "},"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":"1812.02986","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2018-12-07T11:32:08Z","cross_cats_sorted":["math.IT"],"title_canon_sha256":"8da58427d508830c6bb91a14e649dad216f0d6d41b1f2cdf98a5911d1be1068a","abstract_canon_sha256":"e0eadae97e41e138a780609d76207d7543f88d709bd5f0a050ec37b69c1a112e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:58:51.201974Z","signature_b64":"P8xqFhsAL7h+M7/8WCto1vShelMKVazQxXxvpabKe+Gti44OLodcxZh11KEJa9BcnSUgePVm/P1B3y9uGbtSAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b331d422c3a0c1e641a6e671b9237c680c681a981d01e7347361335597d37553","last_reissued_at":"2026-05-17T23:58:51.201519Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:58:51.201519Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Channel Tracking for Wireless Energy Transfer: A Deep Recurrent Neural Network Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Chang-Jae Chun, Dong In Kim, Il-Min Kim, Jae-Mo Kang","submitted_at":"2018-12-07T11:32:08Z","abstract_excerpt":"In this paper, we study channel tracking for the wireless energy transfer (WET) system, which is practically a very important, but challenging problem. Regarding the time-varying channels as a sequence to be predicted, we exploit the recurrent neural network (RNN) technique for channel tracking. Particularly, combining the deep long short-term memory (LSTM) RNN with the deep feedforward neural network, we develop a novel channel tracking scheme for the WET system, which estimates the channel state information (CSI) at the energy transmitter based on the previous CSI estimates, and the current "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.02986","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":""},"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":"1812.02986","created_at":"2026-05-17T23:58:51.201580+00:00"},{"alias_kind":"arxiv_version","alias_value":"1812.02986v1","created_at":"2026-05-17T23:58:51.201580+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.02986","created_at":"2026-05-17T23:58:51.201580+00:00"},{"alias_kind":"pith_short_12","alias_value":"WMY5IIWDUDA6","created_at":"2026-05-18T12:33:01.666342+00:00"},{"alias_kind":"pith_short_16","alias_value":"WMY5IIWDUDA6MQNG","created_at":"2026-05-18T12:33:01.666342+00:00"},{"alias_kind":"pith_short_8","alias_value":"WMY5IIWD","created_at":"2026-05-18T12:33:01.666342+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/WMY5IIWDUDA6MQNG4ZY3SI34NA","json":"https://pith.science/pith/WMY5IIWDUDA6MQNG4ZY3SI34NA.json","graph_json":"https://pith.science/api/pith-number/WMY5IIWDUDA6MQNG4ZY3SI34NA/graph.json","events_json":"https://pith.science/api/pith-number/WMY5IIWDUDA6MQNG4ZY3SI34NA/events.json","paper":"https://pith.science/paper/WMY5IIWD"},"agent_actions":{"view_html":"https://pith.science/pith/WMY5IIWDUDA6MQNG4ZY3SI34NA","download_json":"https://pith.science/pith/WMY5IIWDUDA6MQNG4ZY3SI34NA.json","view_paper":"https://pith.science/paper/WMY5IIWD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1812.02986&json=true","fetch_graph":"https://pith.science/api/pith-number/WMY5IIWDUDA6MQNG4ZY3SI34NA/graph.json","fetch_events":"https://pith.science/api/pith-number/WMY5IIWDUDA6MQNG4ZY3SI34NA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WMY5IIWDUDA6MQNG4ZY3SI34NA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WMY5IIWDUDA6MQNG4ZY3SI34NA/action/storage_attestation","attest_author":"https://pith.science/pith/WMY5IIWDUDA6MQNG4ZY3SI34NA/action/author_attestation","sign_citation":"https://pith.science/pith/WMY5IIWDUDA6MQNG4ZY3SI34NA/action/citation_signature","submit_replication":"https://pith.science/pith/WMY5IIWDUDA6MQNG4ZY3SI34NA/action/replication_record"}},"created_at":"2026-05-17T23:58:51.201580+00:00","updated_at":"2026-05-17T23:58:51.201580+00:00"}