[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82950-en":3,"doc-seo-82950-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},82950,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Contrastive Predictive Coding with Compression for Enhanced Channel State Feedback in Wireless Networks","Accurate and timely channel state information (CSI) is critical for next-generation wireless systems, yet existing research and 3GPP approaches typically treat CSI compression and CSI prediction as separate tasks, leaving channel aging insufficiently handled. A unified compression–prediction framework integrates Contrastive Predictive Coding (CPC) into the 3GPP-compliant CSI compression architecture. The method forecasts future latent representations and jointly optimizes reconstruction fidelity and temporal coherence via a combined 1-SGCS and InfoNCE objective without increasing feedback overhead.","Contrastive Predictive Coding with Compression for Enhanced Channel State Feedback in Wireless Networks  \nAhmed Y. Radwan, Fahad Syed Muhammad, Matthew Baker, and Hina Tabassum, Senior Member, IEEE  \narXiv :2607 .054 19v2 [ cs .IT] 8 Jul 2026  \nAbstract—Accurate and timely channel state information (CSI) is essential for next-generation wireless systems, yet existing works treat CSI compression and CSI prediction as separate problems, both in academia and in current 3GPP studies. Consequently, channel aging remains insufficiently addressed within standardized CSI feedback pipelines. In this article, we propose a unified compression–prediction framework that integrates Contrastive Predictive Coding (CPC) directly into the 3GPP-compliant CSI compression architecture. Instead of predicting high-dimensional CSI matrices, our approach forecasts future latent representations and jointly optimizes reconstruction fidelity and temporal predictive coherence via a combined 1-SGCS and InfoNCE objective. This design enables temporal representation learning without increasing feedback overhead. We present two variants: CPC-before-Compression, which performs autoregressive modeling on encoded features prior to quantization, and CPC-after-Compression, which shifts temporal modeling to the base-station to reduce the complexity of users’devices. Evaluations on 3GPP-compliant datasets from Nokia, Oppo, and CATT show that CPC-before-Compression achieves over 90% reconstruction accuracy with 32× lower decoder GFLOPs than the 3GPP baseline, while CPC-after-Compression preserves an identical encoder footprint and the same 64-bit feedback overhead. By unifying compression and prediction within a standardized pipeline, the proposed framework providesan age-aware, computationally efficient CSI feedback solution.  \nThe source code is publicly available at: [https://github.com/AhmedRadwan02/cpc-3gpp](https://github.com/AhmedRadwan02/cpc-3gpp)  \nIndex Terms—Contrastive predictive coding, 3GPP, CSI feedback, joint CSI compression and prediction.  \nI. INTRODUCTION  \nCHANNEL state information (CSI) characterizes the wire  \nless propagation conditions between transmitters and receivers, and is a fundamental enabler of network resource management mechanisms, such as beamforming, user scheduling, and link adaptation. However, the inherent time-varying nature of wireless channels, compounded by CSI acquisition delays, leads to channel aging, wherein CSI becomes outdated before it can be effectively utilized. This challenge is further exacerbated in 5G and beyond, where massive antenna arrays, highly directional beams, mobile transceivers, and operation at higher frequencies significantly reduce channel coherence time [1], [2] . Consequently, predicting accurate CSI is becoming critical to sustain reliable performance in wireless networks.  \nA. Y. Radwan and H. Tabassum are with the Department of Electrical Engineering and Computer Science, York University, Toronto, ON M3J 1P3, Canada. Emails: {hinat, [ahmedyra](ahmedyra}@yorku.ca. F. Muhammad)[}](ahmedyra}@yorku.ca. F. Muhammad)[@yorku.ca. F. Muhammad](ahmedyra}@yorku.ca. F. Muhammad) is with Nokia Networks, 12 rue Jean Bart, Paris-Saclay, Massy, 91300, France. M. Baker is with the Nokia UK, Broers Building, 21 JJ Thomson Avenue, Cambridge, CB3 0FA, United Kingdom. Emails: {fahad.syed muhammad, [matthew.baker](matthew.baker}@nokia.com. This work was)[}](matthew.baker}@nokia.com. This work was)[@nokia.com. This work was](matthew.baker}@nokia.com. This work was) supported by Natural Sciences and Engineering Research Council of Canada (NSERC) CREATE grant.  \nRecently, the 3rd Generation Partnership Project (3GPP) 1 has taken a leading role in formalizing the integration of artificial intelligence (AI) into wireless networks, with emphasis on improving CSI feedback latency in multi-user multipleinput multiple-output (MU-MIMO) systems [3] . The need for CSI prediction becomes even more evident in high-mobility enviro","cbCaih6psbhp8dAI","https://ap.wps.com/l/cbCaih6psbhp8dAI","pdf",1271684,2,1,9,"English","en",105,"# Introduction\n## CSI compression and prediction in existing work\n## Motivation from 3GPP Release-18\n## Limitations of standalone prediction methods","[{\"question\":\"Why does channel aging remain a problem in current CSI feedback pipelines?\",\"answer\":\"Because many approaches treat CSI compression and CSI prediction separately, CSI can become outdated before it is effectively used. The time-varying channel and acquisition delays make this issue harder in 5G and beyond.\"},{\"question\":\"How does the proposed framework unify compression and prediction?\",\"answer\":\"It integrates Contrastive Predictive Coding (CPC) into a 3GPP-compliant compression architecture by forecasting future latent representations. It then jointly optimizes reconstruction fidelity and temporal predictive coherence using a combined 1-SGCS and InfoNCE objective.\"},{\"question\":\"What are the two model variants and what is their computational implication?\",\"answer\":\"CPC-before-Compression models temporal relationships on encoded features prior to quantization, yielding over 90% reconstruction accuracy and much lower decoder GFLOPs than the 3GPP baseline. CPC-after-Compression moves temporal modeling to the base station to keep the users’ device encoder footprint unchanged while preserving the same 64-bit feedback overhead.\"}]",1784184275,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"contrastive-predictive-coding-with-compression-for-enhanced-channel-state-feedback-in-wireless-networks","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,47,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":20},"https://docshare.wps.com/document/","Document",{"item":48,"name":12,"@type":43,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/contrastive-predictive-coding-with-compression-for-enhanced-channel-state-feedback-in-wireless-networks/82950/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-24","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why does channel aging remain a problem in current CSI feedback pipelines?","Question",{"text":75,"@type":76},"Because many approaches treat CSI compression and CSI prediction separately, CSI can become outdated before it is effectively used. The time-varying channel and acquisition delays make this issue harder in 5G and beyond.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed framework unify compression and prediction?",{"text":80,"@type":76},"It integrates Contrastive Predictive Coding (CPC) into a 3GPP-compliant compression architecture by forecasting future latent representations. It then jointly optimizes reconstruction fidelity and temporal predictive coherence using a combined 1-SGCS and InfoNCE objective.",{"name":82,"@type":73,"acceptedAnswer":83},"What are the two model variants and what is their computational implication?",{"text":84,"@type":76},"CPC-before-Compression models temporal relationships on encoded features prior to quantization, yielding over 90% reconstruction accuracy and much lower decoder GFLOPs than the 3GPP baseline. CPC-after-Compression moves temporal modeling to the base station to keep the users’ device encoder footprint unchanged while preserving the same 64-bit feedback overhead.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,127,130,134],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":22,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]