[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83557-en":3,"doc-seo-83557-105":29,"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":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},83557,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","QuaMoE-DRF Proactive Beam and Rate Adaptation via Multimodal Dynamic Radio Map Forecasting in ISAC Networks","Static radio maps provide location-dependent propagation priors but cannot model short-term blockage from moving objects. Direct sensing-assisted beam prediction is constrained because beam indices omit SINR margins, MCS thresholds, alternative BS options, and neighbor-beam equivalence. The paper presents QuaMoE-DRF, a quality-aware multimodal dynamic radio map forecasting framework for proactive beam and rate adaptation in ISAC. It uses a future beam-SINR field to support threshold-rate BS, beam, MCS, goodput, and outage decisions, achieving higher effective rate and lower outage on a dynamic multi-BS/multi-UE urban benchmark.","QuaMoE-DRF: Proactive Beam and Rate Adaptation via Multimodal Dynamic Radio Map Forecasting in ISAC Networks  \nZhihan Zeng, Graduate Student Member, IEEE, Kaihe Wang, Graduate Student Member, IEEE, Zhongpei Zhang, Chongwen Huang, Senior Member, IEEE  \narXiv :2607 .00974v 1 [ cs .IT] 1 Jul 2026  \nAbstract—Static radio maps provide location-dependent propagation priors, but they cannot capture short-term blockage caused by moving objects. Direct sensing-assisted beam prediction is also limited because a beam index discards SINR margins, MCS thresholds, BS alternatives, and communicationequivalent neighboring beams. This paper proposes QuaMoEDRF, a quality-aware multimodal dynamic radio map forecasting framework for proactive beam and rate adaptation in ISAC networks. Its core representation is a future beam-SINR field. We show that the full multi-BS beam-SINR field is sufficient for finite-codebook threshold-rate BS, beam, MCS, goodput, and outage decisions. For tractability, the implemented model learns a compact reference-BS local field, complemented by BSlevel supervision, joint BS–beam supervision, and latent network context; we also clarify that this compact projection alone is not sufficient for BS association. QuaMoE-DRF fuses static geometry, event-like motion observations, structured sensing states, and wireless history through a quality-aware mixture-of-experts module motivated by inverse-variance fusion under heteroscedastic modality errors. It jointly predicts communication-oriented map channels and proactive BS, beam, and MCS decisions. On a dynamic multi-BS and multi-UE urban benchmark, QuaMoE-DRF achieves 402.5 Mbps effective rate, 0.0417 outage probability, and 0.1836 map RMSE, improving the effective rate by 5.67% and reducing outage by 8.35% over the strongest completed effective-rate baseline. The current validation uses labels from a compact blockage/path-loss simulator, with ray tracing used only for calibration and sanity checking.  \nIndex Terms—Integrated sensing and communication, dynamic radio map, channel knowledge map, multimodal fusion, mixture of experts, dynamic blockage, beam prediction, rate adaptation.  \nI. INTRODUCTION  \nTHE evolution toward sixth-generation (6G) wireless net  \nworks is shifting mobile systems from connection-centric infrastructures to sensing-capable and environment-aware platforms. In the IMT-2030 vision, future networks are expected to provide not only high data rate, low latency, ubiquitous  \nZhihan Zeng and Zhongpei Zhang are with the National Key Laboratory of Wireless Communications, University of Electronic Science and Technology of China, Chengdu 611731, China (e-mail: [202511220608@std.uestc.edu.cn](202511220608@std.uestc.edu.cn); [zhangzp@uestc.edu.cn](zhangzp@uestc.edu.cn)). Kaihe Wang is with the University of Electronic Science and Technology of China, Chengdu 611731, China (e-mail: khe[wang@yeah.net](wang@yeah.net)). Chongwen Huang is with the College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China; with Zhejiang Provincial Key Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing; and also with the National Key Laboratory of Millimeter-Wave and Terahertz Remote Sensing, Hangzhou 310027, China (emails: [chongwenhuang@zju.edu.cn](chongwenhuang@zju.edu.cn)). The corresponding author is Zhongpei Zhang.  \nconnectivity, and high reliability, but also new capabilities enabled by integrated sensing and communication (ISAC), native artificial intelligence, and environment-aware operation [1]–[3], [28] . This shift is especially important in dense urban and vehicular high-frequency networks. Millimeter-wave propagation, large antenna arrays, narrow beams, and frequent link-state changes make conventional reactive control increasingly costly, because beam training, pilot measurements, and feedback must be repeated whenever the propagation condition changes [4]–[10], [26], [27] . A central commu","cbCaia8TdpYLjOXJ","https://ap.wps.com/l/cbCaia8TdpYLjOXJ","pdf",6199797,1,14,"English","en",105,"# Introduction\n## Dynamic blockage and proactive adaptation\n## Radio maps and channel knowledge maps","[{\"question\":\"Why do static radio maps and direct sensing-assisted beam prediction struggle in dense mobile scenarios?\",\"answer\":\"Static radio maps cannot reflect short-term blockage caused by moving objects, while beam-index-based prediction discards key communication information such as SINR margins, MCS thresholds, BS alternatives, and neighboring-beam equivalence.\"},{\"question\":\"What is the core representation used by QuaMoE-DRF for proactive decisions?\",\"answer\":\"QuaMoE-DRF represents future channel quality as a future beam-SINR field, showing that the full multi-BS beam-SINR field supports BS, beam, MCS, goodput, and outage decisions under finite-codebook threshold-rate systems.\"},{\"question\":\"How does QuaMoE-DRF combine multimodal inputs to improve dynamic radio map forecasting?\",\"answer\":\"It fuses static geometry, event-like motion observations, structured sensing states, and wireless history using a quality-aware mixture-of-experts module motivated by inverse-variance fusion under heteroscedastic modality 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do static radio maps and direct sensing-assisted beam prediction struggle in dense mobile scenarios?","Question",{"text":75,"@type":76},"Static radio maps cannot reflect short-term blockage caused by moving objects, while beam-index-based prediction discards key communication information such as SINR margins, MCS thresholds, BS alternatives, and neighboring-beam equivalence.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the core representation used by QuaMoE-DRF for proactive decisions?",{"text":80,"@type":76},"QuaMoE-DRF represents future channel quality as a future beam-SINR field, showing that the full multi-BS beam-SINR field supports BS, beam, MCS, goodput, and outage decisions under finite-codebook threshold-rate systems.",{"name":82,"@type":73,"acceptedAnswer":83},"How does QuaMoE-DRF combine multimodal inputs to improve dynamic radio map forecasting?",{"text":84,"@type":76},"It fuses static geometry, event-like motion observations, structured sensing states, and 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