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Cooperative Multi-Monostatic Sensing for Object Localization in 6G Networks

Maximiliano Rivera Figueroa, Pradyumna Kumar Bishoyi, Marina Petrova

TL;DR

This work tackles passive object localization in 6G by deploying cooperative multi-monostatic sensing with FR1 5G-NR BSs. Each BS performs ToA-based range estimation via a periodogram, and a central CPU fuses these estimates using ML, MAP, or NLLS while assigning weights derived from Gaussian fits to mitigate multipath. Ray-tracing simulations show that fusion improves range and positioning accuracy, particularly at higher bandwidths, though BS placement and multipath richness influence gains. The approach demonstrates a practical pathway to passive sensing in bandwidth-limited sub-6 GHz regimes with potential for scalable network-assisted localization in future wireless systems.

Abstract

Enabling passive sensing of the environment using cellular base stations (BSs) will be one of the disruptive features of the sixth-generation (6G) networks. However, accurate localization and positioning of objects are challenging to achieve as multipath significantly degrades the reflected echos. Existing localization techniques perform well under the assumption of large bandwidth available but perform poorly in bandwidth-limited scenarios. To alleviate this problem, in this work, we introduce a 5G New Radio (NR)-based cooperative multi-monostatic sensing framework for passive target localization that operates in the Frequency Range 1 (FR1) band. We propose a novel fusion-based estimation process that can mitigate the effect of multipath by assigning appropriate weight to the range estimation of each BS. Extensive simulation results using ray-tracing demonstrate the efficacy of the proposed multi-sensing framework in bandwidth-limited scenarios.

Cooperative Multi-Monostatic Sensing for Object Localization in 6G Networks

TL;DR

This work tackles passive object localization in 6G by deploying cooperative multi-monostatic sensing with FR1 5G-NR BSs. Each BS performs ToA-based range estimation via a periodogram, and a central CPU fuses these estimates using ML, MAP, or NLLS while assigning weights derived from Gaussian fits to mitigate multipath. Ray-tracing simulations show that fusion improves range and positioning accuracy, particularly at higher bandwidths, though BS placement and multipath richness influence gains. The approach demonstrates a practical pathway to passive sensing in bandwidth-limited sub-6 GHz regimes with potential for scalable network-assisted localization in future wireless systems.

Abstract

Enabling passive sensing of the environment using cellular base stations (BSs) will be one of the disruptive features of the sixth-generation (6G) networks. However, accurate localization and positioning of objects are challenging to achieve as multipath significantly degrades the reflected echos. Existing localization techniques perform well under the assumption of large bandwidth available but perform poorly in bandwidth-limited scenarios. To alleviate this problem, in this work, we introduce a 5G New Radio (NR)-based cooperative multi-monostatic sensing framework for passive target localization that operates in the Frequency Range 1 (FR1) band. We propose a novel fusion-based estimation process that can mitigate the effect of multipath by assigning appropriate weight to the range estimation of each BS. Extensive simulation results using ray-tracing demonstrate the efficacy of the proposed multi-sensing framework in bandwidth-limited scenarios.
Paper Structure (17 sections, 12 equations, 6 figures, 1 table)

This paper contains 17 sections, 12 equations, 6 figures, 1 table.

Table of Contents

  1. Introduction
  2. System Model
  3. Transmit Signal Model
  4. Received Echo Signal
  5. ToA and Doppler Shift Estimation
  6. Multi-Monostatic Sensing
  7. Fusion Algorithms
  8. ML Estimator
  9. MAP Estimator
  10. NLLS Estimator
  11. Position Estimation
  12. Performance Evaluation
  13. Setup and Scenarios
  14. Simulation Results and Discussion
  15. Estimation error with fusion of two BSs
  16. ...and 2 more sections

Figures (6)

  • Figure 1: Illustration of an urban environment involving multiple Base Stations (BSs), a target (vehicle), and a Central Processing Unit (CPU).
  • Figure 2: Delay profile comparison between a LOS-only and a multipath-rich environment.
  • Figure 3: Example of the ray-tracing simulator for a given target's position. An open street map (OSM) from Berlin, PLZ 10969, Germany, is used. The intersection corresponds to Charlottenstrasse with Zimmerstrasse. The colour code indicates the path loss.
  • Figure 4: Distance and positioning error for different BW for $K = 2$.
  • Figure 5: Positioning error for different number of BSs fused for $BW = 20$ MHz.
  • ...and 1 more figures