[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84369-en":3,"doc-seo-84369-105":29,"detail-sidebar-cat-0-en-105":90},{"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":4,"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},84369,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Spatio-Temporal Scheduling Prediction Under Backhaul Delay for Resilient Coordinated Beamforming","Coordinated beamforming in distributed 5G depends on timely inter-cell scheduling exchange, yet backhaul latency makes scheduling information stale and can reduce coordinated performance below an uncoordinated baseline even after one TTI. A two-stage predictive framework is proposed: StemGNN predicts future UE scheduling states from delayed historical observations, replacing stale inputs to the CBF-SLNR precoder. On a three-cell massive MIMO downlink with Quadriga UMi channels, StemGNN reaches 87.57% mean prediction accuracy, recovering 57–73% of sum-rate loss and up to 83% of Lag-1 fairness loss.","Spatio-Temporal Scheduling Prediction Under Backhaul Delay for Resilient Coordinated Beamforming  \nPrashant Kumar Singh􀀌1 ,2[0009−0006−4222−678X], Shubham Vaishnav 1[0000−0001−7612−4227], Ahmet Hasim Gökceoglu2[0000−0002−2706−1079], and Li Wang2[0000−0003−3365−2403]  \n1 Stockholm University, Stockholm, Sweden  \n{prku7110, [shubham.vaishnav}@dsv.su.se](shubham.vaishnav}@dsv.su.se)  \n2 Huawei R&D, Stockholm, Sweden  \n{ahmet.hasim.gokceoglu1, [leo.li.wang}@huawei.com](leo.li.wang}@huawei.com)  \narXiv :2607 .08454v 1 [ cs .NI] 9 Jul 2026  \nAbstract. Coordinated beamforming in distributed 5G networks relies on the timely exchange of inter-cell scheduling information, but backhaul latency makes this information stale. Even a single transmission time interval (TTI) of delay can reduce CBF-SLNR performance below the uncoordinated baseline, because the precoder suppresses interference toward users that are no longer active. Coordination on stale information is therefore worse than no coordination at all. To address this, we propose a two-stage predictive framework in which a Spectral Temporal Graph Neural Network (StemGNN) predicts future user equipment (UE) scheduling states from delayed historical observations, and the predictions replace stale inputs to the CBF-SLNR precoder. Evaluated on a three-cell massive MIMO downlink with 60 UEs and 64 antennas per base station under Quadriga Urban Micro (UMi) channels and a proportional fair scheduler, StemGNN achieves a mean scheduling prediction accuracy of 87.57%, outperforming LSTM, GRU, Simple RNN, and Markov chain baselines at all evaluated horizons, with gains of up to 7.71% over LSTMat longer horizons where inter-UE structural dependencies dominate over temporal autocorrelation. When integrated into coordinated beamforming, the predictions recover 57–73% of the sum rate loss caused by one TTI of backhaul delay, improving sum rate by 9 .58–14.35% over the noprediction baseline and recovering up to 83% of the Lag-1 fairness loss for cell-edge users, with fairness gains persisting at higher lag values where throughput gains diminish. These results show that treating backhaul latency as a spatio-temporal forecasting problem is an effective approach for robust inter-cell coordination in delay-constrained networks.  \nKeywords: Coordinated beamforming · StemGNN · Scheduling prediction · Backhaul delay · Edge AI  \n2 P. K. Singh et al.  \n1 Introduction  \nModern 5G networks, and emerging 6G ones, use coordinated beamforming (CBF) across multiple base stations (BSs) to reduce inter-cell interference and keep spectral efficiency high [10,27,5,17] . In distributed radio access networks, neighboring BSs exchange channel state and scheduling information over backhaul links. These links have limited bandwidth, are often asynchronous, and frequently add delay [25,3] . As a result, the view each BS has of the wider network is almost always out of date, while user activity changes much faster: users enter, leave, or change their scheduling state continuously [29,15] . Standard CBF algorithms such as Weighted Minimum Mean Square Error (WMMSE) optimization [27,26] assume that each BS has fresh, global channel and scheduling information. Once this assumption breaks, the sum rate drops and service quality becomes uneven across cells.  \nFrom an edge AI point of view, each BS is an autonomous edge node that has to make real-time, latency-critical decisions using incomplete and partially outdated information about its peers. Backhaul latency, jitter, and asynchronous updates act as a kind of partial fault on the links between nodes: the information a BS needs is not lost outright, it just arrives late and stale. A beamforming controller at the edge therefore needs to stay robust against this kind of information-staleness fault to keep service available and quality of experience consistent across cells.  \nThe trust side of this problem matters too. Each BS feeds its beamformer with scheduling and channel reports i","cbCaijRO4VTJjMKM","https://ap.wps.com/l/cbCaijRO4VTJjMKM","pdf",330336,1,18,"English","en",105,"# Abstract\n# Introduction\n## Problem of stale inter-cell scheduling due to backhaul delay\n## Edge AI perspective and robustness requirements\n## Trust and adversarial-message considerations\n## Limitations of prior work and motivation for spatio-temporal prediction","[{\"question\":\"Why can coordinated beamforming perform worse than uncoordinated transmission under backhaul delay?\",\"answer\":\"Backhaul latency makes inter-cell scheduling information stale, so the precoder suppresses interference toward users that may no longer be active, reducing CBF-SLNR performance below the uncoordinated baseline.\"},{\"question\":\"How does StemGNN help enable resilient coordinated beamforming?\",\"answer\":\"StemGNN predicts future UE scheduling states using delayed historical observations, and those predictions replace stale inputs to the CBF-SLNR precoder.\"},{\"question\":\"What improvements are reported when predictions are integrated into coordinated beamforming?\",\"answer\":\"Predictions recover 57–73% of the sum-rate loss caused by one TTI of backhaul delay, improve sum rate by 9.58–14.35% over the no-prediction baseline, and recover up to 83% of Lag-1 fairness loss for cell-edge users.\"}]",1784195148,45,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"spatio-temporal-scheduling-prediction-under-backhaul-delay-for-resilient-coordinated-beamforming","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/spatio-temporal-scheduling-prediction-under-backhaul-delay-for-resilient-coordinated-beamforming/84369/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-16",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Why can coordinated beamforming perform worse than uncoordinated transmission under backhaul delay?","Question",{"text":74,"@type":75},"Backhaul latency makes inter-cell scheduling information stale, so the precoder suppresses interference toward users that may no longer be active, reducing CBF-SLNR performance below the uncoordinated baseline.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does StemGNN help enable resilient coordinated beamforming?",{"text":79,"@type":75},"StemGNN predicts future UE scheduling states using delayed historical observations, and those predictions replace stale inputs to the CBF-SLNR precoder.",{"name":81,"@type":72,"acceptedAnswer":82},"What improvements are reported when predictions are integrated into coordinated beamforming?",{"text":83,"@type":75},"Predictions recover 57–73% of the sum-rate loss caused by one TTI of backhaul delay, improve sum rate by 9.58–14.35% over the no-prediction baseline, and recover up to 83% of Lag-1 fairness loss for 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