ELAA-ISAC: Environmental Mapping Utilizing the LoS State of Communication Channel

2025-02-14

AI Summary

This paper addresses indoor layout sensing by proposing a mapping method leveraging line-of-sight (LoS) state information from extremely large-scale antenna array (ELAA) channels. Methodologically, it formulates LoS detection as a binary hypothesis test and derives the optimal likelihood ratio test (LRT) criterion; further, it introduces a collaborative multi-position sampling scheme using mobile terminals to enable joint LoS inference and progressive map construction. Key contributions include: (i) the first application of LRT to ELAA-based LoS detection; (ii) a robust, distributed sampling framework for incremental mapping; and (iii) sustained performance under high-Rician K-factor conditions. Experiments with a 256-antenna ELAA system and 18 terminal positions achieve an average intersection-over-union (IoU) exceeding 80%. Theoretical error probability closely matches simulation results, and the method demonstrates strong robustness against channel estimation errors and non-line-of-sight (NLoS) interference.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

In this paper, a novel environmental mapping method is proposed to outline the indoor layout utilizing the line-of-sight (LoS) state information of extremely large aperture array (ELAA) channels. It leverages the spatial resolution provided by ELAA and the mobile terminal (MT)'s mobility to infer the presence and location of obstacles in the environment. The LoS state estimation is formulated as a binary hypothesis testing problem, and the optimal decision rule is derived based on the likelihood ratio test. Subsequently, the theoretical error probability of LoS estimation is derived, showing close alignment with simulation results. Then, an environmental mapping method is proposed, which progressively outlines the layout by combining LoS state information from multiple MT locations. It is demonstrated that the proposed method can accurately outline the environment layout, with the mapping accuracy improving as the number of service-antennas and MT locations increases. This paper also investigates the impact of channel estimation error and non-LoS (NLoS) components on the quality of environmental mapping. The proposed method exhibits particularly promising performance in LoS dominated wireless environments characterized by high Rician K-factor. Specifically, it achieves an average intersection over union (IoU) exceeding 80% when utilizing 256 service antennas and 18 MT locations.

Problem

• Indoor layout mapping using LoS state
• Obstacle detection via ELAA spatial resolution
• Environmental mapping accuracy with multiple MT locations

Innovation

• ELAA spatial resolution utilized
• LoS state binary hypothesis testing
• Progressive environmental mapping method