{"id":"https://openalex.org/W7160527272","doi":"https://doi.org/10.48550/arxiv.2605.04400","title":"Contextual Memory-Enhanced Source Coding for Low-SNR Communications","display_name":"Contextual Memory-Enhanced Source Coding for Low-SNR Communications","publication_year":2026,"publication_date":"2026-05-06","ids":{"openalex":"https://openalex.org/W7160527272","doi":"https://doi.org/10.48550/arxiv.2605.04400"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.04400","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.04400","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.04400","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135567826","display_name":"Ziqiong Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Ziqiong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135547796","display_name":"Rongpeng Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Rongpeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10901","display_name":"Advanced Data Compression Techniques","score":0.2818000018596649,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10901","display_name":"Advanced Data Compression Techniques","score":0.2818000018596649,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12131","display_name":"Wireless Signal Modulation Classification","score":0.16750000417232513,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.10809999704360962,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.6819999814033508},{"id":"https://openalex.org/keywords/source-code","display_name":"Source code","score":0.6692000031471252},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.45820000767707825},{"id":"https://openalex.org/keywords/coding","display_name":"Coding (social sciences)","score":0.4474000036716461},{"id":"https://openalex.org/keywords/distributed-source-coding","display_name":"Distributed source coding","score":0.4399999976158142},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.41269999742507935},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.3930000066757202},{"id":"https://openalex.org/keywords/multiple-description-coding","display_name":"Multiple description coding","score":0.3871999979019165},{"id":"https://openalex.org/keywords/variable-length-code","display_name":"Variable-length code","score":0.3734000027179718}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7336999773979187},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.6819999814033508},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.6692000031471252},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.45820000767707825},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.4474000036716461},{"id":"https://openalex.org/C200801453","wikidata":"https://www.wikidata.org/wiki/Q5283181","display_name":"Distributed source coding","level":4,"score":0.4399999976158142},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.43070000410079956},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.42559999227523804},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.41269999742507935},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.3930000066757202},{"id":"https://openalex.org/C135554599","wikidata":"https://www.wikidata.org/wiki/Q3682206","display_name":"Multiple description coding","level":3,"score":0.3871999979019165},{"id":"https://openalex.org/C60603091","wikidata":"https://www.wikidata.org/wiki/Q2981616","display_name":"Variable-length code","level":3,"score":0.3734000027179718},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.3675999939441681},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.362199991941452},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.33500000834465027},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.32850000262260437},{"id":"https://openalex.org/C56985126","wikidata":"https://www.wikidata.org/wiki/Q854039","display_name":"Rayleigh fading","level":4,"score":0.32739999890327454},{"id":"https://openalex.org/C81081738","wikidata":"https://www.wikidata.org/wiki/Q55542","display_name":"Lossless compression","level":3,"score":0.3050000071525574},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.2976999878883362},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.27630001306533813},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.27070000767707825},{"id":"https://openalex.org/C165021410","wikidata":"https://www.wikidata.org/wiki/Q55564","display_name":"Lossy compression","level":2,"score":0.2700999975204468},{"id":"https://openalex.org/C161765866","wikidata":"https://www.wikidata.org/wiki/Q184748","display_name":"Codec","level":2,"score":0.2671000063419342},{"id":"https://openalex.org/C2776836416","wikidata":"https://www.wikidata.org/wiki/Q1364844","display_name":"False alarm","level":2,"score":0.2648000121116638},{"id":"https://openalex.org/C2778095710","wikidata":"https://www.wikidata.org/wiki/Q6031225","display_name":"Information source (mathematics)","level":2,"score":0.2637999951839447},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.26269999146461487},{"id":"https://openalex.org/C157899210","wikidata":"https://www.wikidata.org/wiki/Q1395022","display_name":"Convolutional code","level":3,"score":0.26019999384880066},{"id":"https://openalex.org/C761482","wikidata":"https://www.wikidata.org/wiki/Q118093","display_name":"Transmission (telecommunications)","level":2,"score":0.25209999084472656}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.04400","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.04400","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.04400","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.04400","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"While":[0],"Separate":[1],"Source-Channel":[2],"Coding":[3,53,108],"(SSCC)":[4],"retains":[5],"the":[6,23,45,80,89,134,139,180,198,216,219],"practical":[7],"benefits":[8],"of":[9,25,36,91,200,218],"modular":[10],"system":[11],"design,":[12],"its":[13],"effectiveness":[14,217],"in":[15],"noisy":[16],"text":[17],"transmission":[18],"is":[19],"fundamentally":[20],"constrained":[21],"by":[22,132],"fragility":[24,90],"autoregressive":[26,173],"source":[27,92,129,136,141,174,190,201],"decoding.":[28],"In":[29],"low-SNR":[30],"regimes,":[31],"even":[32],"a":[33,105,128,146,160],"small":[34],"number":[35],"residual":[37,96,204],"bit":[38],"errors":[39],"after":[40],"channel":[41,67,72,97,205],"decoding":[42,68,202],"may":[43],"derail":[44],"subsequent":[46],"lossless":[47],"reconstruction":[48],"process,":[49],"especially":[50],"when":[51],"Arithmetic":[52],"(AC)":[54],"relies":[55],"on":[56,71],"Large":[57],"Language":[58],"Model":[59],"(LLM)-based":[60],"probability":[61,93,191],"estimation.":[62],"Existing":[63],"remedies":[64],"either":[65],"strengthen":[66],"based":[69],"solely":[70],"observations":[73],"or":[74],"introduce":[75],"contextual":[76,125],"information":[77],"only":[78,179],"at":[79,184],"receiver":[81],"for":[82,111],"post-hoc":[83],"correction,":[84],"yet":[85],"neither":[86],"fully":[87],"addresses":[88],"modeling":[94],"under":[95],"errors.":[98,206],"To":[99],"this":[100,102],"end,":[101],"paper":[103],"proposes":[104],"Memory-Augmented":[106],"Source":[107],"(MASC)":[109],"scheme":[110,221],"robust":[112],"SSCC-based":[113],"transmission.":[114],"Rather":[115],"than":[116],"treating":[117],"context":[118],"as":[119],"external":[120],"side":[121],"information,":[122],"MASC":[123,144,188],"internalizes":[124],"patterns":[126],"into":[127],"model":[130],"shared":[131,147],"both":[133],"transmitter-side":[135],"encoder":[137],"and":[138,157,196,212],"receiver-side":[140],"decoder.":[142],"Specifically,":[143],"employs":[145],"Parameterized":[148],"Contextual":[149],"Memory":[150],"(PCM)":[151],"to":[152,164,203],"encode":[153],"multi-order":[154],"$n$-gram":[155],"patterns,":[156],"further":[158],"introduces":[159],"Mixture-of-Memory-Experts":[161],"Router":[162],"(MMER)":[163],"perform":[165],"sparse,":[166],"hidden-state-dependent":[167],"routing":[168],"over":[169,209],"memory":[170],"experts":[171],"during":[172],"modeling.":[175],"By":[176],"adaptively":[177],"activating":[178],"most":[181],"relevant":[182],"memories":[183],"each":[185],"coding":[186],"step,":[187],"refines":[189],"estimation,":[192],"shortens":[193],"average":[194],"codelength,":[195],"mitigates":[197],"sensitivity":[199],"Extensive":[207],"experiments":[208],"Rayleigh":[210],"fading":[211],"AWGN":[213],"channels":[214],"demonstrate":[215],"proposed":[220],"compared":[222],"with":[223],"state-of-the-art":[224],"methods.":[225]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-08T00:00:00"}
