Time-varying Wireless Channel Tracking with Online Parameter Learning via the Birth-Death-Drift Model

2026-06-23

AI Summary

This work addresses the challenge of accurately acquiring time-varying channel state information in massive MIMO systems under dynamic propagation environments with low pilot overhead, a task hindered by the unrealistic assumptions often made in existing approaches. To overcome this limitation, the paper proposes a unified framework based on a birth-death-drift (BDD) model that integrates vector approximate message passing (VAMP) with the expectation-maximization (EM) algorithm, enabling online, adaptive channel tracking without requiring prior knowledge of model parameters. The proposed method relaxes the common reliance on independent and identically distributed Gaussian sensing matrices, effectively captures temporal channel correlations, and automatically estimates BDD parameters. Simulation results demonstrate that, under practical conditions such as model mismatch, the proposed scheme significantly outperforms existing benchmark algorithms while maintaining robustness and engineering applicability.

Abstract

Accurate massive MIMO channel state information (CSI) acquisition with low pilot overhead is critical in dynamic propagation environments. Exploiting temporal correlation is key to reducing pilot overhead, yet most existing methods often rely on impractical assumptions. The approximate message passing with side information (AMP-SI) algorithm, built upon a birth-death-drift (BDD) model, represents a significant step in this direction. However, its practical deployment is hindered by three major limitations: reliance on i.i.d. Gaussian sensing matrices, need for perfect BDD parameter knowledge, and a statistically approximate treatment of temporal information. To address these limitations, we introduce BDD-VAMP-EM, a fully automated algorithm that relies on the BDD model, vector AMP (VAMP), and expectation-maximization (EM) in a unified framework. Simulations show that BDD-VAMP-EM consistently outperforms existing benchmarks, particularly under model parameter mismatch, confirming its practical viability.

Problem

• massive MIMO
• channel state information
• pilot overhead
• temporal correlation
• time-varying channel

Innovation

• Birth-Death-Drift model
• Vector AMP
• Expectation-Maximization
• Channel State Information
• Online Parameter Learning