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

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.