Channel estimation improves afdm communication in high-speed settings
Model-Order-Adaptive Channel Estimation for AFDM Systems with Fractional Delay and Doppler
Information Theory
Summary
Accurately understanding a communication channel is crucial, especially when devices move quickly and signals get distorted by delays and Doppler effects. The authors studied a way to better estimate the signal paths and their characteristics for a new waveform called AFDM, which is good for fast-moving situations. They developed a smart method that adapts to how many signal paths exist and improves detection by using pilot signals and statistical tests. Their approach works well even when speeds reach up to 600 km/h, resulting in clearer communication.
What this means in practice
- •For wireless communication engineers: Enhance channel estimation algorithms for high-mobility communication systems using AFDM waveforms to improve signal clarity at very high speeds.
- •For satellite communication teams: Improve channel state detection and correction in satellite links experiencing fractional delays and Doppler shifts.
Authors
Ruizhe Wang, Cunhua Pan, Hong Ren, Shaohua Li, Jiangzhou Wang
Abstract
Affine frequency division multiplexing (AFDM) has emerged as a promising waveform for high-mobility communications owing to its ability to exploit multipath diversity. However, channel state information (CSI) acquisition remains challenging in channels with fractional delay and Doppler and an unknown number of propagation paths. In this paper, we investigate the channel estimation for AFDM systems. We first analyze the AFDM response in the presence of fractional-delay-induced frequency wrapping. The analysis reveals that fractional delay may displace the dominant extremum and generate informative secondary extrema. Then, we develop a model-order-adaptive channel estimator within an improved space-alternating generalized expectation-maximization (SAGE) framework. A dual-pilot reference symbol is employed for path and delay initialization, while pilot observations across multiple AFDM symbols provide temporal information for Doppler estimation. New paths are identified through statistically controlled residual tests, whereas unsupported or redundant paths are removed by conditional support pruning. The delay, Doppler frequency, and complex gain of each retained path are subsequently estimated through a coarse-to-fine procedure and refined using the exact AFDM likelihood. Simulation results demonstrate that the proposed method achieves lower normalized mean squared error (NMSE) and bit error rate (BER) than representative SAGE, sparse-recovery, and Bayesian benchmarks. It also provides reliable path detection and model-order estimation and maintains robust performance at terminal velocities of up to 600~km/h.