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Location-Agnostic Channel Knowledge Map Construction for Dynamic Scenes

Kequan Zhou, Guangyi Zhang, Hanlei Li, Yunlong Cai, Guanding Yu

TL;DR

This paper proposes a novel framework named location-agnostic dynamic CKM (LAD-CKM), constructed through dynamic radio frequency (RF) radiance field rendering, which takes instantaneous uplink CSI and partial downlink CSI as inputs and yields significant performance gains compared with existing baselines in terms of effective data rate.

Abstract

To alleviate the pilot and CSI-feedback burden in 6G, channel knowledge map (CKM) has emerged as a promising approach that predicts CSI solely from user locations. Nevertheless, accurate location information is rarely available in current systems. Moreover, the uncertainty inherent to highly dynamic scenes further degrades the performance of existing schemes that typically assume quasi-static scenarios. In this paper, we propose a novel framework named location-agnostic dynamic CKM (LAD-CKM). Specifically, LAD-CKM is constructed through dynamic radio frequency (RF) radiance field rendering, which takes instantaneous uplink CSI and partial downlink CSI as inputs. To enable effective rendering, a dedicated radiator representation network (RARE-Net) is designed to capture the spatial-spectral correlations within the inputs. Furthermore, an adaptive deformation module is devised to deform the uplink CSI-based queries of RARE-Net according to instantaneous channel dynamics, thereby enhancing CSI prediction accuracy under mobility. In addition, a novel synthetic channel dataset is created in outdoor dynamic scenes via ray-tracing. Simulation results demonstrate that LAD-CKM yields significant performance gains compared with existing baselines in terms of effective data rate.

Location-Agnostic Channel Knowledge Map Construction for Dynamic Scenes

TL;DR

This paper proposes a novel framework named location-agnostic dynamic CKM (LAD-CKM), constructed through dynamic radio frequency (RF) radiance field rendering, which takes instantaneous uplink CSI and partial downlink CSI as inputs and yields significant performance gains compared with existing baselines in terms of effective data rate.

Abstract

To alleviate the pilot and CSI-feedback burden in 6G, channel knowledge map (CKM) has emerged as a promising approach that predicts CSI solely from user locations. Nevertheless, accurate location information is rarely available in current systems. Moreover, the uncertainty inherent to highly dynamic scenes further degrades the performance of existing schemes that typically assume quasi-static scenarios. In this paper, we propose a novel framework named location-agnostic dynamic CKM (LAD-CKM). Specifically, LAD-CKM is constructed through dynamic radio frequency (RF) radiance field rendering, which takes instantaneous uplink CSI and partial downlink CSI as inputs. To enable effective rendering, a dedicated radiator representation network (RARE-Net) is designed to capture the spatial-spectral correlations within the inputs. Furthermore, an adaptive deformation module is devised to deform the uplink CSI-based queries of RARE-Net according to instantaneous channel dynamics, thereby enhancing CSI prediction accuracy under mobility. In addition, a novel synthetic channel dataset is created in outdoor dynamic scenes via ray-tracing. Simulation results demonstrate that LAD-CKM yields significant performance gains compared with existing baselines in terms of effective data rate.
Paper Structure (13 sections, 5 equations, 7 figures)

This paper contains 13 sections, 5 equations, 7 figures.

Table of Contents

  1. Introduction
  2. System Model
  3. Wireless Channel Model
  4. Performance Metric
  5. Construction of LAD-CKM
  6. Construction Pipeline
  7. Virtual Radiator Sampling
  8. Adaptive Query Generation
  9. RARE-Net Rendering
  10. Spatial-Aware Backbone
  11. Frequency-Aware Fine-Tuning
  12. Simulation Results
  13. Conclusion

Figures (7)

  • Figure 1: Propagation modeling for the radiated signal from a wireless radiator.
  • Figure 2: The construction pipeline of LAD-CKM.
  • Figure 3: The architecture of the ADM.
  • Figure 4: The architecture of the RARE-Net. (a) provides an overview of RARE-Net's entire architecture. (b) and (c) depict the inner structures of the frequency-attention layer and residual blocks within the RARE-Net, respectively.
  • Figure 5: The dynamic campus scene for channel dataset generation.
  • ...and 2 more figures