In this paper, a wireless channel sounding campaign was conducted in a land-to-ship scenario at 3.2GHz. The ship, equipped with a transmitter (TX), passed islands during measurement while the stationary receiver (RX) remained on land. Based on the measured data, the wireless channel characteristics in scenarios featuring islands were analyzed and a channel model was built. The power delay profile (PDP) indicates that as the TX moves to the shore, the relative delay of clusters shifts linearly backward on the delay axis. Additionally, navigating through islands leads to a significant increase in the number of multipath components (MPCs). The single-frequency bias displayed in the Doppler spectrum can provide an insightful dimension for clustering. For large-scale fading, a two-ray model is used to characterize path loss which captures deep fading phenomenon specific to maritime environments. For small-scale fading, we develop a clustered delay line model that shows a linear relationship between cluster delay and TX-RX distance, with excess delay of MPCs following an exponential distribution. Our analysis reveals that the best-fit distribution for small-scale fading in line-of-sight (LOS) conditions shifts from Ricean to log-normal as the ship passes through islands. Furthermore, the number and power of clusters/MPCs are modeled. This research offers valuable insights for the design of maritime communication systems, particularly in environments with islands.

Most AI-based channel estimation methods with static environment assumptions suffer from performance degradation due to the distribution shift caused by the varying channel environment. As one of the solutions, transfer learning also faces the problem of catastrophic forgetting, where the model tents to fail in previous estimation tasks after learning from new ones. In this paper, we propose a continuous learning-based channel estimation (CLCE) scheme that integrates a series of subnetworks to preserve historical knowledge and achieve an ongoing process of self-improvement. To determine whether the wireless environment is previously unobserved, we first propose a distance-based unsupervised out-of-distribution (OOD) detection algorithm to perceive the distribution shift of the channel environment. The OOD detection algorithm is developed based on the representation of channel data in the latent space of a variational autoencoder (VAE), which is designed to infer the latent variable that implies the characteristics of the wireless environment. Then, a new channel estimation subnetwork is initiated with meta-learning to adapt to the dynamic channel environments with a small set of OOD channel data. Simulation results reveal that our proposed scheme can accurately detect unobserved channel environments without introducing additional detection network, and efficiently adapt to them with few online samples. Furthermore, the mean square error (MSE) result of the channel estimations across multiple environments demonstrates that CLCE effectively mitigates catastrophic forgetting and outperforms the competitors.

In this paper, variational sparse Bayesian learning is utilized to estimate the multipath parameters for wireless channels. Due to its flexibility to fit any probability density function (PDF), the Gaussian mixture model (GMM) is introduced to represent the complicated fading phenomena in various communication scenarios. First, the expectation-maximization (EM) algorithm is applied to the parameter initialization. Then, the variational update scheme is proposed and implemented for the channel parameters’ posterior PDF approximation. Finally, in order to prevent the derived channel model from overfitting, an effective pruning criterion is designed to eliminate the virtual multipath components. The numerical results show that the proposed method outperforms the variational Bayesian scheme with Gaussian prior in terms of root mean squared error (RMSE) and selection accuracy of model order.