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

Jiuyu Liu, Chunmei Xu, Yi Ma, Rahim Tafazolli, Ahmed Elzanaty

TL;DR

This work introduces a communication-centric approach to indoor environmental mapping by exploiting the LoS state information embedded in ELAA channels. It formulates LoS-state estimation as a binary hypothesis test and derives an optimal threshold along with a closed-form error probability, then proposes an environmental mapping algorithm that progressively reconstructs the layout by aggregating LoS states across multiple MT locations. The mapping performance improves with more service antennas and more MT locations, achieving IoU values above 80% in LoS-dominated regimes (K > 15 dB) when using 256 antennas and 18 MT locations. This framework enables sensing capabilities integrated with communication infrastructure, with practical guidance on how channel estimation errors and NLoS components influence mapping quality, and suggests future work on wideband signaling and moving targets.

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.

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

TL;DR

This work introduces a communication-centric approach to indoor environmental mapping by exploiting the LoS state information embedded in ELAA channels. It formulates LoS-state estimation as a binary hypothesis test and derives an optimal threshold along with a closed-form error probability, then proposes an environmental mapping algorithm that progressively reconstructs the layout by aggregating LoS states across multiple MT locations. The mapping performance improves with more service antennas and more MT locations, achieving IoU values above 80% in LoS-dominated regimes (K > 15 dB) when using 256 antennas and 18 MT locations. This framework enables sensing capabilities integrated with communication infrastructure, with practical guidance on how channel estimation errors and NLoS components influence mapping quality, and suggests future work on wideband signaling and moving targets.

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.

Paper Structure

This paper contains 12 sections, 3 theorems, 23 equations, 5 figures.

Table of Contents

  1. Introduction
  2. ELAA Channel Model, Channel Estimation, and Problem Statement
  3. ELAA Channel Model
  4. Channel Estimation
  5. Problem Statement
  6. The Proposed Environmental Mapping Method
  7. LoS State Estimation
  8. Environmental Mapping Algorithm
  9. Simulation and Numerical Results
  10. Conclusion and Outlook
  11. Proof of Proposition \ref{['prop01']}
  12. Proof of Corollary \ref{['cor02']}

Key Result

Proposition 1

Given $\hat{h}_{m}$, and assumptions A1) and A2), the decision rule for LoS state estimation is expressed as follows where $\hat{b}_{m}$ denotes the estimated LoS state, and $\Theta_{m}$ is the threshold for the test. The optimal $\Theta_{m}$ that minimizes the error probability of LoS state estimation is given by where $P(\mathcal{H}_{0})$ and $P(\mathcal{H}_{1})$ are the prior probabilities of

Figures (5)

  • Figure 1: An indoor environment deployed with an ELAA system. The object being sensed may obstruct the LoS path between the and ELAA antennas.
  • Figure 2: Progressive environmental mapping with perfect information across $7$ locations. The mapped area incrementally approximates the actual environment as the user traverses locations 1-7, illustrating enhanced spatial perception through increased sampling points.
  • Figure 3: Average-IoU performance versus the number of locations for varying quantities of ELAA antennas. It can be found that increasing the number of antennas enhances environmental mapping accuracy.
  • Figure 4: Error rate of LoS state estimation as a function of $\gamma_{v}$ for different Rician $K$-factors. The simulation results closely match the theoretical error probability derived in Corollary \ref{['cor02']}.
  • Figure 5: Average-IoU performance as a function of $\gamma_{v}$ for different values of the Rician $K$-factor. The results demonstrate an inverse relationship between the mapping accuracy and the magnitude of NLoS components plus channel estimation errors.

Theorems & Definitions (7)

  • Proposition 1
  • Remark 1
  • Remark 2
  • Corollary 1
  • Remark 3
  • Corollary 2
  • Remark 4