[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82554-en":3,"doc-seo-82554-105":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},82554,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Mobile Base Station Positioning in Smart Ports Based on Kriged Sparse Measurements and Obstacle Inference","Smart-port wireless networks face severe, time-varying radio blockage from container stacks and industrial structures, which makes mobile integrated access and backhaul (MIAB) deployment difficult without reliable environmental awareness. The DOCKING framework converts sparse radio measurements into optimization-ready obstacle representations by reconstructing radio environment maps (REMs) with Ordinary Kriging, then approximating dominant attenuation regions using compact cuboidal blockage. The inferred geometry drives backhaul-aware joint MIAB placement, UE association, and backhaul selection. Results show REM prediction errors below 3 dB at the 90th percentile with only 15% sampling, obstacle coverage above 85% true positive, capacity gains up to 150%, and fast genetic optimization (5–15 s), supported by field measurements.","arXiv :2607 .00709v 1 [ cs .NI] 1 Jul 2026  \nReceived XX Month, XXXX; revised XX Month, XXXX; accepted XX Month, XXXX; Date of publication XX Month, XXXX; date of  \ncurrent version XX Month, XXXX.  \nDigital Object Identifier 10.1109/OJCOMS.2026.XXXXXX  \nMobile Base Station Positioning in Smart Ports Based on Kriged Sparse Measurements and Obstacle Inference  \nPAULO FURTADO CORREIA1 , ANDR ´E COELHO1 (Member, IEEE), and MANUEL  \nRICARDO1 (Member, IEEE)  \n1 INESC TEC, Faculdade de Engenharia da Universidade do Porto, Portugal  \nCORRESPONDING AUTHOR: Paulo Furtado Correia (e-mail: [paulo.j.correia@inesctec.pt](paulo.j.correia@inesctec.pt)).  \nThis work is co-financed by Component 5 – Capitalization and Business Innovation integrated in the Resilience Dimension of the Recovery and Resilience Plan within the scope of the Recovery and Resilience Mechanism (MRR) of the European Union (EU), framed in the Next Generation EU, for the period 2021 – 2026, within project NEXUS, with reference 53 .  \nABSTRACT Smart-port wireless networks suffer from severe and dynamic radio blockage caused by container stacks and industrial structures, making efficient mobile integrated access and backhaul (MIAB) deployment challenging without accurate environmental awareness. Existing approaches typically rely on prior obstacle maps, explicit geometry information, or computationally intensive propagation models that limit adaptability in operational deployments. This paper presents DOCKING, a radio environment map (REM)-driven framework that converts sparse radio measurements into optimizationready obstacle representations for MIAB deployment. The proposed approach infers propagation-relevant obstacle abstractions directly from reconstructed REMs, avoiding the need for obstacle-geometry databases while relying on known network parameters and sparse radio measurements. Sparse reference signal received power (RSRP) and signal-to-interference-plus-noise ratio (SINR) observations are reconstructed through Ordinary Kriging (OKG), after which dominant attenuation regions are identified and approximated by compact cuboidal blockage characterization. The inferred geometry is incorporated into a backhaulaware optimization stage that jointly determines MIAB placement, user-equipment (UE) association, and backhaul selection. Under realistic smart-port conditions, REM reconstruction achieves prediction errors below 3dB at the 90th percentile using only 15% spatial sampling, while obstacle characterization exceeds 85% true-positive coverage. Capacity gains reach up to 150% in sparse deployment scenarios, and a fast Genetic Algorithm converges within 5–15s per network snapshot. A proof-of-concept field campaign further corroborates the proposed REM-to-obstacle-to-MIAB workflow using real measurements, showing throughput trends consistent with the optimization predictions. These results demonstrate that sparse radio measurements can provide sufficient environmental awareness to support practical obstacle-aware MIAB deployment in obstruction-prone industrial environments.  \nINDEX TERMS Integrated access and backhaul (IAB), mobile integrated access and backhaul (MIAB), radio environment maps (REM), ordinary kriging, obstacle characterization, smart ports, genetic algorithms  \nI. INTRODUCTION  \nTHE transition toward sixth-generation (6G) wireless  \nsystems is driven by the need for reliable, high-capacity, and adaptive connectivity in dynamic environments [1] . Among these, smart seaports are undergoing rapid digital transformation through industrial Internet of Things (IIoT) sensing, autonomous guided vehicles (AGVs), remote crane  \nsystems, real-time asset tracking, and dense video surveillance [2], [3] . These services place sustained pressure on radio resources and make network planning a critical challenge.  \nDespite the comparatively favorable propagation characteristics of sub-6 GHz bands, seaport environments present substantial challenges arising from their physica","cbCaifJV6J0r2yyg","https://ap.wps.com/l/cbCaifJV6J0r2yyg","pdf",20411361,1,15,"English","en",105,"# Introduction\n## Smart-port connectivity challenges\n## Motivation and research gap\n# Proposed DOCKING framework\n## Sparse REM reconstruction with Ordinary Kriging\n## Obstacle inference and cuboidal characterization\n## Backhaul-aware optimization for MIAB deployment","[{\"question\":\"What problem does DOCKING address in smart ports?\",\"answer\":\"It addresses efficient MIAB deployment under dynamic radio blockage caused by container stacks and industrial structures, where prior obstacle maps or full site surveys are impractical.\"},{\"question\":\"How are sparse measurements turned into obstacle representations?\",\"answer\":\"DOCKING reconstructs radio environment maps from sparse RSRP and SINR observations using Ordinary Kriging, then infers propagation-relevant dominant attenuation regions and approximates them as compact cuboidal blockages.\"},{\"question\":\"What is jointly optimized in the MIAB deployment stage?\",\"answer\":\"The framework performs backhaul-aware optimization that jointly determines MIAB placement, user-equipment (UE) association, and backhaul 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problem does DOCKING address in smart ports?","Question",{"text":75,"@type":76},"It addresses efficient MIAB deployment under dynamic radio blockage caused by container stacks and industrial structures, where prior obstacle maps or full site surveys are impractical.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are sparse measurements turned into obstacle representations?",{"text":80,"@type":76},"DOCKING reconstructs radio environment maps from sparse RSRP and SINR observations using Ordinary Kriging, then infers propagation-relevant dominant attenuation regions and approximates them as compact cuboidal blockages.",{"name":82,"@type":73,"acceptedAnswer":83},"What is jointly optimized in the MIAB deployment stage?",{"text":84,"@type":76},"The framework performs backhaul-aware optimization that jointly determines MIAB placement, user-equipment (UE) association, and backhaul 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