Mobile Base Station Positioning in Smart Ports Based on Kriged Sparse Measurements and Obstacle Inference

2026-07-01

AI Summary

This work addresses the challenge of dynamic wireless blockage caused by container stacking and industrial infrastructure in smart ports, which severely hinders the efficient deployment of Mobile Integrated Access and Backhaul (MIAB) base stations. To tackle this, the authors propose DOCKING—a framework that reconstructs RSRP/SINR radio environment maps using ordinary Kriging interpolation from sparse wireless measurements and known network parameters, without requiring prior geometric information about obstacles. Strong attenuation regions are abstracted into compact cuboid models to drive backhaul-aware joint MIAB optimization. This approach uniquely integrates radio environment map reconstruction with obstacle inference for industrial MIAB deployment. Experimental results demonstrate that with only 15% spatial sampling, the 90th-percentile REM prediction error remains below 3 dB, obstacle detection achieves a true positive rate exceeding 85%, system capacity improves by up to 150% under sparse deployment, and each optimization converges within 5–15 seconds, with measured throughput closely matching predictions.

Abstract

Smart-port wireless networks suffer from dynamic radio blockage caused by container stacks and industrial structures, challenging efficient mobile integrated access and backhaul (MIAB) deployment. Existing approaches rely on obstacle maps, geometry information, or computationally intensive propagation models that limit adaptability. This paper presents DOCKING, a radio environment map (REM)-driven framework that converts sparse radio measurements into optimization-ready obstacle representations for MIAB deployment. The framework infers propagation-relevant obstacle abstractions from reconstructed REMs, eliminating the need for obstacle-geometry databases while relying only on known network parameters and sparse measurements. Reference signal received power (RSRP) and signal-to-interference-plus-noise ratio (SINR) observations are reconstructed using Ordinary Kriging (OKG), and dominant attenuation regions are approximated by compact cuboidal blockage models. The inferred geometry feeds a backhaul-aware optimization that determines MIAB placement, user equipment (UE) association, and backhaul selection. Under realistic smart-port conditions, REM reconstruction achieves prediction errors below 3 dB at the 90th percentile using only 15% spatial sampling, while obstacle characterization exceeds 85% true-positive coverage. Capacity gains reach 150% in sparse deployments, and a fast Genetic Algorithm converges within 5-15 s per network snapshot. A field campaign using real measurements validates the workflow, showing throughput trends consistent with optimization predictions. Results demonstrate that sparse radio measurements provide sufficient environmental awareness for practical obstacle-aware MIAB deployment in obstruction-prone industrial environments.

Problem

• Mobile Base Station Positioning
• Smart Ports
• Radio Blockage
• Integrated Access and Backhaul
• Obstacle Inference

Innovation

• Radio Environment Map
• Ordinary Kriging
• Obstacle Inference
• MIAB Deployment
• Sparse Measurements