Dynamic Channel Knowledge Map Construction in MIMO-OFDM Systems

2025-12-29

AI Summary

In dynamic propagation environments, channel knowledge maps (CKMs) for MIMO-OFDM systems suffer from severe misalignment due to strong coupling among scatterer motion, antenna rotation, and synchronization errors. Method: This paper proposes the first time-varying channel model integrating both quasi-static and dynamic scatterers, coupled with a two-stage approximate Bayesian inference algorithm: Stage I jointly calibrates synchronization errors and learns priors from historical data; Stage II performs low-latency posterior estimation using minimal real-time measurements. Contribution/Results: The approach overcomes the conventional CKM’s reliance on quasi-static assumptions, achieving significantly improved channel estimation accuracy under limited measurements. It reduces pilot overhead and CKM update latency, establishing a scalable, real-time CKM construction paradigm for intelligent wireless sensing and communication in high-mobility scenarios.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Calibration & Uncertainty Quantification
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Channel knowledge map (CKM) is a promising paradigm for environment-aware communications by establishing a deterministic mapping between physical locations and channel parameters. Existing CKM construction methods focus on quasi-static propagation environment. This paper develops a dynamic CKM construction method for multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. We establish a dynamic channel model that captures the coexistence of quasi-static and dynamic scatterers, as well as the impacts of antenna rotation and synchronization errors. Based on this model, we formulate the problem of dynamic CKM construction within a Bayesian inference framework and design a two-stage approximate Bayesian inference algorithm. In stage I, a high-performance algorithm is developed to jointly infer quasi-static channel parameters and calibrate synchronization errors from historical measurements. In stage II, by leveraging the quasi-static parameters as informative priors, a low-complexity algorithm is designed to estimate dynamic parameters from limited real-time measurements. Simulation results validate the superiority of the proposed method and demonstrate its effectiveness in enabling low-overhead, high-performance channel estimation in dynamic environments.

Problem

• Develops dynamic CKM construction for MIMO-OFDM systems
• Models coexistence of quasi-static and dynamic scatterers
• Enables low-overhead channel estimation in dynamic environments

Innovation

• Dynamic channel model for MIMO-OFDM with static and moving scatterers
• Two-stage Bayesian inference algorithm for parameter estimation
• Low-complexity real-time estimation using informative priors from history