Dynamic Channel Knowledge Map Construction in MIMO-OFDM Systems

TL;DR

This paper introduces a novel dynamic channel knowledge map construction method for MIMO-OFDM systems that models both static and dynamic scatterers, improving real-time channel estimation accuracy in changing environments.

Contribution

It develops a Bayesian inference-based framework and algorithms for dynamic CKM construction that account for environmental changes, antenna rotation, and synchronization errors.

Findings

Proposed method outperforms existing static models in dynamic scenarios.

Achieves high-accuracy channel estimation with low measurement overhead.

Demonstrates robustness to synchronization errors and environmental variations.

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.