Learning to Gridize: Segment Physical World by Wireless Communication Channel

2025-07-21

AI Summary

Existing gridding approaches rely on inaccessible location information or flawed assumptions—such as equating signal strength similarity with channel similarity—leading to suboptimal efficiency and accuracy in large-scale network spatial partitioning. This paper proposes the Channel-Space Gridding (CSG) framework, the first to jointly model channel estimation and spatial gridding, enabling semantic partitioning of physical space via beam-level reference signals. We design a novel CSG Autoencoder (CSG-AE), integrating a learnable RSRP-to-CAPS encoder, a sparse codebook quantizer, and a physics-informed channel decoder, optimized end-to-end via the PIDA training paradigm to jointly refine angular power spectrum estimation and clustering. Evaluated on both synthetic and real-world datasets, CSG achieves a 30% reduction in Active MAE and a 65% reduction in Overall MAE for RSRP prediction, significantly improving channel consistency and clustering balance.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: State Estimation
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

Gridization, the process of partitioning space into grids where users share similar channel characteristics, serves as a fundamental prerequisite for efficient large-scale network optimization. However, existing methods like Geographical or Beam Space Gridization (GSG or BSG) are limited by reliance on unavailable location data or the flawed assumption that similar signal strengths imply similar channel properties. We propose Channel Space Gridization (CSG), a pioneering framework that unifies channel estimation and gridization for the first time. Formulated as a joint optimization problem, CSG uses only beam-level reference signal received power (RSRP) to estimate Channel Angle Power Spectra (CAPS) and partition samples into grids with homogeneous channel characteristics. To perform CSG, we develop the CSG Autoencoder (CSG-AE), featuring a trainable RSRP-to-CAPS encoder, a learnable sparse codebook quantizer, and a physics-informed decoder based on the Localized Statistical Channel Model. On recognizing the limitations of naive training scheme, we propose a novel Pretraining-Initialization-Detached-Asynchronous (PIDA) training scheme for CSG-AE, ensuring stable and effective training by systematically addressing the common pitfalls of the naive training paradigm. Evaluations reveal that CSG-AE excels in CAPS estimation accuracy and clustering quality on synthetic data. On real-world datasets, it reduces Active Mean Absolute Error (MAE) by 30% and Overall MAE by 65% on RSRP prediction accuracy compared to salient baselines using the same data, while improving channel consistency, cluster sizes balance, and active ratio, advancing the development of gridization for large-scale network optimization.

Problem

• Segment space into grids with similar channel characteristics
• Overcome limitations of location-based or signal-strength gridization methods
• Improve accuracy and consistency in wireless channel estimation

Innovation

• Proposes Channel Space Gridization (CSG) framework
• Develops CSG Autoencoder with trainable components
• Introduces PIDA training scheme for stability